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[Assistant] We'll start by contrasting Elasticsearch with a relational database system.


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[Assistant] We then look at some compelling reasons to use or


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[Assistant] Avoid using Elasticsearch. We round up this section by examining why Elasticsearch makes for a great in addition to system.


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[Assistant] In this video, we start by looking at features of Elasticsearch that separates it from the relational database system.


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[Assistant] We next look at why Elasticsearch should not be used as a primary database.


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[Assistant] We then look at key dos and don'ts when deciding to use Elasticsearch.


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[Assistant] Afterwards, we look at why speed is the biggest consideration for using Elasticsearch.


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[Assistant] Finally, we highlight some closing points to help in deciding when to use Elasticsearch.


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[Assistant] Let's take out a few features that clearly separates Elasticsearch from a relational database system, for example.


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[Assistant] Elasticsearch is a highly available, denormalized distributed search engine, which is optimized for search and retrieval.


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[Assistant] To the contrary, a relational database system is designed with granular security


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[Assistant] Relational databases excel in cases that involve many rights.


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[Assistant] Relational databases also enforce referential integrity and uniqueness. Most important, relational databases thrive in cases of normalization.


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[Assistant] Now let's examine why Elasticsearch most likely doesn't make the best primary database. We do this by looking at transactions.


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[Assistant] Though Apache Lucene has a form of Transactions Incorporated, this is not the case with Elasticsearch.


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[Assistant] Transaction in database systems is generally defined as a sequence of operations that account for one unit of work. So with Elasticsearch, for example,


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[Assistant] Rolling back to previously submitted documents is not possible. If you think about it, removing transaction makes a lot of sense as the trade-off for having transactions is less speed and Elasticsearch is all about speed. So, no transactions equals more speed.


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[Assistant] Elasticsearch in many respects is a document-oriented database where documents must be denormalized before being indexed. Denormalization


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[Assistant] Which makes it increasingly difficult to maintain up-to-date documents. As such, no normalization equals much greater speed.


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[Assistant] By definition, a database should be robust, particularly if it serves as your primary data store. So.


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[Assistant] A primary database should be able to cancel resource intensive queries on command.


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[Assistant] As a side note, if you started running a BigQuery in Elasticsearch and then decided to cancel it, this is not possible.


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[Assistant] Equally important, your primary datastore should always work unless otherwise specified. Elasticsearch simply doesn't deal with


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[Assistant] Out of memory errors in the same manner as a robust database system.


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[Assistant] Outer memory errors can and will bring an Elasticsearch cluster to its knees. Something you clearly do not want in a production environment.


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[Assistant] By definition, security and databases is the wide-range use of mechanisms to ensure data protection, integrity, and availability.


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[Assistant] Elasticsearch has no feature for authentication or authorization.


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[Assistant] So with Elasticsearch, unlike with robust database systems, there is no way to configure authorization levels to, say, restrict access to a given table based on a user profile.


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[Assistant] Having said that, though Elasticsearch doesn't provide built-in security, XPAC does, however, add a granular security layer over Elasticsearch, which allows for access control that ranges from who can check cluster health


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[Assistant] All the way down to restricting access to individual fields.


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[Assistant] So that's definitely a plus from a security perspective on the Elasticsearch side.


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[Assistant] When to use Elasticsearch. Elasticsearch is most effectively used in conjunction with another database as this allows you to leverage the full power of search while simultaneously enjoying the robustness of a mature relational database system.


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[Assistant] In this model, the database maintains the master records while asynchronously pushing records to Elasticsearch when not to use Elasticsearch. Conversely, in cases where real-time data is required, like updated financial statements, etc.,


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[Assistant] Or in the cases where granular security features, a lot of updates, or there's a need for transactions or integrity constraints, elastic search is not the best option. Some might argue that systems like Postgres can implement full text search.


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[Assistant] Though this is true, please note that the performance difference between Postgres and Elastic Search when it comes to full text search is huge.


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[Assistant] Denormalization has the many reasons why elastic search is extremely fast. But, I'd like you to consider the following.


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[Assistant] While running the same hardware, queries that require a given number of seconds using SQL can be achieved with Elasticsearch at a rate of about 1000 times faster. That's 5 seconds on SQL compared to 5 milliseconds on Elasticsearch. Now that's fast. Now I'm going to go through some closing points.


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[Assistant] Such as with web and our application logs via logstash.


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[Assistant] Elasticsearch is quite effective as a primary storage system, but please


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[Assistant] Remember that this is generally seen as an exception.


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[Assistant] Now let's look at some things that Elasticsearch is good for. Elasticsearch is good for querying a large data set for exact matches in select fields, filtering results based on an initial query,


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[Assistant] It is important to remember that Elasticsearch is great for doing the specific task of indexing and searching massive datasets.


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[Assistant] And finally, Elasticsearch is arguably the best search engine for time-based aggregations and handling geodata points, two of the most modern use cases. And that concludes this section.


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[Assistant] Next we looked at aspects of Elasticsearch which makes it less suitable as a primary database system.


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[Assistant] We followed up that by emphasizing why speed makes Elasticsearch the technology that it is. And finally, we concluded this section by driving home some key points that further speak to why Elasticsearch makes for the best in addition to system. And that concludes this section. Up next


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[Assistant] We look at the Elasticsearch application stack, known as the Elastic Stack.


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[Assistant] The Olympic Games will be held in our country in four years' time.


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[Assistant] As a great many people will be visiting the country.


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[Assistant] The government will be building new hotels, an immense stadium and a new Olympic standard swimming pool.


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[Assistant] They will also be building new roads and a special railway line.


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[Assistant] The games will be held just outside the capital.


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[Assistant] And the whole area will be called Olympic City.


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[Assistant] Workers will have completed the new roads by the end of this year.


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[Assistant] By the end of next year they will have finished work on the new stadium.


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[Assistant] The fantastic modern buildings have been designed by Kurt Gunter.


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[Assistant] Everybody will be watching anxiously as the new buildings go up.


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[Assistant] Okay! See ya, StoryBots! Come back soon! Just five billion more years before I can retire.


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[Assistant] Welcome to this video in learning Elasticsearch 5.0 entitled XPAC.


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[Assistant] And during the implementation segment, you get some hands-on experience with installing and running XPAC. So what is XPAC? Well,


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[Assistant] XPAC combines the powerful features of Elasticsearch into one streamlined bundle. Features of XPAC include security,


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[Assistant] XPAC adds a granular security level which allows for restrictions from the general cluster level, for example controlling who can view cluster health, all the way down to restricting access to a given document field. Role management is a key feature that highlights the security of XPAC. And then there's alerting. Alerting keeps you in the know about cluster activities.


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[Assistant] For instance, you can set alerts for social media trends, statistical anomaly detection, etc.


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[Assistant] Next there's monitoring. Monitoring gives you a consistent overview of everything ranging from cluster health to real-time analysis of cluster performance.


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[Assistant] Finally, for everything ranging from fraud detection to social media relationship analysis, understanding relationships in Elasticsearch is made easier with graphs. Now, let's walk through getting XPAC up and running.


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[Assistant] And this is the command for installing XPAC on Elasticsearch. Afterwards, you start Elasticsearch. You have to follow the same procedure with Kibana. So first you install it, and then you start up Kibana. One final note on XPAC. First, the default username and password


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[Assistant] You will need to include the username and password in all future command line queries as such. Let's get to XPAC.


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[Assistant] What we need to do is run the command to install the plugin. The command to run the plugin.


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[Assistant] Okay, you select yes and then it's installed. Now what you want to do is you want to start up Elasticsearch.


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[Assistant] Okay, yes, and then we go to the Kibana directory.


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[Assistant] Again, cabana is not running, so what we want to do is we want to run the bin cabana-plugin.


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[Assistant] Okay, this is going to take a bit, so we're going to speed forward.


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[Assistant] Now that xpax is finished installing on cabana, what we're going to do is we're going to start up cabana. So we just go bin, cabana.


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[Assistant] And there you go. Now we have cabana loading up. As you can see here now.


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[Assistant] Unlike before, Kibana requires a user ID and password. Per the presentation,


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[Assistant] Our user ID or username is elastic. And the password is change me. Because you should probably change me.


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[Assistant] As you can see now we have monitoring included in Kibana.


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[Assistant] But it's also a complete full on interface for cluster management. We have overview, we have the number of nodes running.


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[Assistant] We look at the lightweight data shipper known as Beats.


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[Assistant] If something's got your back on, worry not. Just ask the StoryBots. Just ask the StoryBots.


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[Assistant] Would you like to go for a ride in my car?


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[Assistant] One of the challenges with building machine learning systems is that there are so many things you could try, so many things you could change, including, for example, so many hyperparameters you could tune.


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[Assistant] One of the things I've noticed about the most effective machine learning people is they're very clear-eyed about what to tune in order to try to achieve what effect. This is a process we call orthogonalization. Let me tell you what I mean.


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[Assistant] Here's a picture of an old school television with a lot of knobs that you could tune to adjust the picture in various ways.


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[Assistant] So for these old TV sets, maybe there was one knob to adjust


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[Assistant] How tall vertically your image is, another knob to adjust how wide it is, maybe another knob to adjust how, you know, trapezoidal it is, another knob to adjust how much to move the picture left and right, another one to adjust how much the picture is rotated, and so on.


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[Assistant] And what TV designers had spent a lot of time doing was to build the circuitry, really often analog circuitry back then, to make sure that each of the knobs had a reasonably interpretable


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[Assistant] Function, such as one knob to tune this, one knob to tune this, one knob to tune this, and so on.


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[Assistant] Times how trapezoidal the image is, plus 0.8.


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[Assistant] times the position of the image on the horizontal axis and so on.


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[Assistant] If you tune this knob, then the height of the image, the width of the image, how trapezoidal it is, how much it shifts, it all changes all at the same time.


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[Assistant] If you had a knob like that, it'd be almost impossible to tune the TV so that the picture gets centered in the display area. So in this context, orthogonalization refers to


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[Assistant] That the TV designers had designed the knobs so that each knob kind of does only one thing and this makes it much easier to tune the TV so that, you know, the picture gets centered where you want it to be. Here's another example of orthogonalization.


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[Assistant] If you think about learning to drive a car, a car has three main controls, which are steering. The steering wheel decides how much you go left or right, acceleration, and braking.


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[Assistant] So these three controls, or really one control for steering and another, you know, two controls for your speed, it makes it relatively interpretable what


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[Assistant] Your different actions through different controls will do to your car.


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[Assistant] But now imagine if someone were to build a car so that there was a joystick where one axis of the joystick controls 0.3 times your steering angle minus 0.8 times your speed and you had a different control that, uhm,


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[Assistant] In theory, by tuning these two knobs, you could get your car to steer at the angle and at the speed you want, but it's much harder than if you had just one single control for controlling the steering angle and, you know, a separate distinct set of controls for controlling the speed.


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[Assistant] So the concept of a whole organization refers to that if you think of one dimension of what you want to do as controlling your steering angle and another dimension as controlling your speed, then you want one knob


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[Assistant] To just affect the steering angle as much as possible. And another knob, in the case of the car, is really acceleration braking that controls your speed.


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[Assistant] But if you have a control that mixes the two together, like a control like this one that affects both your steering angle and your speed, something that changes both at the same time, then it becomes much harder to set the car to the speed and angle you want. And by having orthogonal,


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[Assistant] Orthogonal means at 90 degrees to each other by having orthogonal controls.


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[Assistant] That are ideally aligned with the things you actually want to control, it makes it much easier to tune the knobs you have to tune, to tune the steering wheel angle and your accelerator you're braking, to get the car to do what you want.


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[Assistant] For a supervised learning system to do well, you usually need to tune the knobs of your system to make sure that four things hold true.


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[Assistant] First is that you usually have to make sure that you're at least doing well on the training set. So performance on the training set needs to pass some acceptability inspection. For some applications, this might mean doing comparably to human level performance.


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[Assistant] But this will depend on your application and we'll talk more about comparing to human level performance next week.


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[Assistant] But after doing well on the training set, you then hope that this leads to also doing well on the dev set.


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[Assistant] And you then hope that this also does well on the test set. And finally, you hope that doing well on the test set on the cost function results in your system performing in the real world. So you hope that this results in, you know, happy cat picture, uh, app users. For example.


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[Assistant] So to relate back to the TV tuning example, if the picture of your TV was either too wide or too narrow, you wanted one knob to tune in order to adjust that. You don't want to have to carefully adjust five different knobs which can also affect different things. You want one knob to just affect the width of your TV image.


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[Assistant] So, in a similar way, if your algorithm is not fitting the training set well on the cost function, you want, you know, one knob, yes, that's my attempt to draw a knob, or maybe one specific set of knobs that you can use to make sure you can tune your algorithm to make it fit well on the training set. So the knobs you use to tune this are, you might train a bigger network, or you might


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[Assistant] Switch to a better optimization algorithm, like the Adam optimization algorithm.


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[Assistant] And so on, and there's some other options we'll discuss later this week and next week.


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[Assistant] In contrast, if you find that the algorithm is not fitting the depth set well, then there's a separate set of knobs. I guess that's my...


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[Assistant] Not very artistic rendering of another knob. Uhm, you wanna have a distinct set of knobs to try.


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[Assistant] So, for example, if your algorithm is not doing well on the dev set, it's doing well on the training set but not on the dev set, then you have a set of knobs around


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[Assistant] So the analogy is now, now that you've tuned the width of your TV set, if the height of the image isn't quite right, then you want a different knob in order to tune the height of the TV image. And you want to either do this, hopefully without affecting the width of your TV image too much. And getting a bigger training set would be another knob you could use, uhm, that helps your


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[Assistant] Now, having adjusted, you know, the width and height of your TV image, well, what if it doesn't meet the third criteria? What if you do well on the dev set but not on the test set?


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[Assistant] If that happens, then the knob you tune is, you probably want to get a bigger depth set. Because uhm, if it does well on the depth set, but not on the test set, it probably means you've over tuned to your depth set. You need to go back and find a bigger depth set.


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[Assistant] And finally, if it does well on the test set, but it isn't delivering to you a happy cat picture app user, then what that means is that, uhm, you want to go back and change either the dev set or the, uh, cost function.


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[Assistant] Because if doing well on the test set according to some cost function doesn't correspond to your algorithm doing what you needed to do in the real world, then it means that either your def test set distribution isn't set correctly or your cost function isn't measuring the right thing.


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[Assistant] I know I'm going over these examples quite quickly, but we'll go much more into detail on these specific knobs later this week and next week.


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[Assistant] So if you aren't following all the details right now, don't worry about it. But I want to give you a sense of this orthogonalization process, that you want to be very clear about which of these, maybe four issues, the different things you could tune, are trying to address. And when I train a neural network, I tend not to use early stocking.


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[Assistant] It's not a bad technique, quite a lot of people do it, but I personally find early stopping difficult to think about because this is a knob that simultaneously affects how well you fit the training set, because if you stop early, you fit the training set less well.


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[Assistant] This is also, simultaneously, uhm, is often done to improve your devset performance. So this is one knob.


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[Assistant] That is, you know, less orthogonalized because it simultaneously affects two things. It's like a knob that simultaneously affects both the width and the height of your TV image.


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[Assistant] And it doesn't mean it's a bad knob to use, you could use it if you want, but when you have more orthogonalized controls,


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[Assistant] Such as these other ones that I'm writing down here, then it just makes the process of tuning your network much easier.


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[Assistant] So I hope that gives you a sense of what orthogonalization means. Just like when you look at a TV image, it's nice if you can say, oh, my TV image is too wide, so I'm going to tune this knob, or it's too tall, so I'm going to tune that knob, or it's too trapezoidal, so I'm going to have to tune that knob.


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[Assistant] In machine learning, it's not easy if you can look at your system and say, oh, this piece of it is wrong, like it's not doing well on the training set, it's not doing well on the dev set, it's not doing well on the test set, or it's doing well on the test set but it's just not in real world. But figure out exactly what's wrong and then have exactly one knob or a specific set of knobs that helps you just solve that problem that is limiting the performance of your machine learning system.


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[Assistant] So what we're going to do this week and next week is go through how to diagnose what exactly is the bottleneck to your system's performance, as well as identify the specific set of knobs you could use to tune your system to improve that aspect of its performance. So let's start going more into the details of this process.


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[Assistant] Ashley, you, you need to like, take a break from life.


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[Assistant] Well now, I spent my time brushing those teeth, night after night, morning after morning. And well, it just became... Kind of annoying. Kind of annoying?


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[Assistant] Lesson 45 A Clear Conscience


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[Assistant] The whole village soon learned that a large sum of money had been lost.


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[Assistant] had lost his wallet while taking his savings to the post office.


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[Assistant] Sam was sure that the wallet must have been found by one of the villagers. But it was not returned to him.


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[Assistant] Sam found his wallet outside his front door. It had been wrapped up in newspaper


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[Assistant] And it contained half the money he had lost. Together with a note which said


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[Assistant] Some more money was sent to Sam with another note. Only 25% a thief now. In time, all Sam's money was paid back in this way. The last note said, I am 100% honest now.


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[Assistant] The term human level performance is sometimes used casually in research articles. But let me show you how we can define it a bit more precisely and in particular use the definition of the phrase human level performance that is most useful for helping you drive progress in your machine learning project.


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[Assistant] So remember from our last video that one of the uses of this phrase, human level error, is that it gives us a way of estimating


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[Assistant] Base error. What is the best possible error any function could either now or in the future ever, ever achieve?


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[Assistant] So bearing that in mind, let's look at a medical image classification example.


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[Assistant] Let's say that you want to look at a radiology image like this and make a, uhm, diagnosis classification decision.


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[Assistant] And suppose that a typical human, untrained human achieves 3% around the stars.


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[Assistant] A typical doctor, maybe a typical radiologist, doctor.


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[Assistant] How should you define human level error? Is human level error 3%, 1%, 0.7% or 0.5%?


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[Assistant] Feel free to pause this video to think about it if you wish.


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[Assistant] And to answer that question, I would urge you to bear in mind that one of the most useful ways to think of human error is as a proxy or an estimate for Bayes' error.


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[Assistant] So, please feel free to pause this video to think about it for a while if you wish. But uhm,


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[Assistant] Here's how I would define human level error, which is if you want a proxy or an estimate for Bayes error, then given that a team of experienced doctors discussing and debating


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[Assistant] But we know the optimal error cannot be higher than 0.5%.


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[Assistant] So what I would do in this setting is use 0.5% as our


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[Assistant] Estimate for Bayes Error. So I would define human level performance as, uhm, 0.5%.


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[Assistant] At least if you're hoping to use human level error in the analysis of bias and variance as we saw in the last video.


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[Assistant] Now, for the purpose of publishing a research paper or for the purpose of deploying a system, maybe there's a different definition of human level error that you can use, which is, so long as you surpass the performance of a typical doctor,


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[Assistant] That seems like maybe a very useful result to have accomplished and maybe surpassing a single radiologist, a single doctor's performance might mean the system is good enough to deploy in some context.


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[Assistant] And if it is to show that you can surpass a single human and therefore argue for deploying your system in some context, maybe this is the appropriate definition. But if your goal is the proxy for Bayes' error, then this is the appropriate definition.


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[Assistant] To see why this matters, let's look at an error analysis example.


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[Assistant] Let's say for a medical imaging diagnosis example that your training error is 5% and your death error is 6%.


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[Assistant] And in the example from the previous slide, human level performance, and I'm going to think of this as proxy for Bayes' error.


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[Assistant] Depending on whether you defined it as a typical doctor's performance or an experienced doctor or a team of doctors, you would have either 1% or


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[Assistant] And remember also our definitions from the previous video that this gap between Bayes Error, or the estimate of Bayes Error and training error, I was calling that a measure of the avoidable bias.


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[Assistant] And this as a measure or an estimate of how much of a variance problem you have in your learning algorithm.


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[Assistant] So in this first example, whichever of these choices you make,


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[Assistant] The measure of avoidable bias would be something like 4%. It would be somewhere between, I guess, 4% if you take that to 4.5%, if you use 0.5%, whereas this is 1%.


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[Assistant] So in this example, I would say it doesn't really matter which of the definitions of human level error you use, whether you use the typical doctor's error or the single experienced doctor's error or the team of experienced doctor's error.


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[Assistant] Whether this is a 4% or 4.5%, this is clearly bigger than the variance problem. And so in this case, you should focus on bias reduction techniques, such as train a bigger network. Now let's look at a second example.


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[Assistant] Let's say your training error is 1% and your dev error is 5%.


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[Assistant] Then again, it doesn't really matter, seems a bit academic, whether the human level performance is 1% or 0.7% or 0.5%, because whichever of these definitions you use, your measure of avoidable bias will be...


[User] Please transcribe this text

[Assistant] I guess somewhere between 0%, if you use that to...


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[Assistant] Uhm, 0.5%, right, that's the gap between the human level performance and your trading error, whereas this gap is 4%. So this 4% is going to be much bigger than the avoidable bias either way, and so, you know, this suggests you should focus on


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[Assistant] There is reduction techniques, such as regularization or getting a bigger training set.


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[Assistant] But where it really matters will be if your training error is 0.7%, so you're doing really well now, and your dev error is 0.8%. In this case, it really matters that


[User] Please transcribe this text

[Assistant] You use, you estimate for Bayes Error as 0.5%.


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[Assistant] Because in this case, your measure of how much avoidable bias you have is 0.2%, which is twice as big.


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[Assistant] As your measure for your variance, which is just 0.1%.


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[Assistant] And so this suggests that maybe both the bias and variance are both problems, but maybe the avoidable bias is a bit bigger of a problem. And in this example, 0.5%, as we discussed on the previous slide, was the best measure of Bayes' error.


[User] Please transcribe this text

[Assistant] Because a team of human doctors can achieve that performance. If you use 0.7 as your proxy for Bayes Error, you would have estimated avoidable buyers as, you know, pretty much zero percent. Uhm, and you might have missed that. You actually should try to do better on your training set.


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[Assistant] So I hope this gives a sense also of why making progress in machine learning problem gets harder as you achieve or as you approach human level performance.


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[Assistant] In this example, once you've approached 0.7% error, unless you're very careful about estimating Bayes error, you might not know how far away you are from Bayes error, and therefore how much you should be trying to reduce avoidable bias.


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[Assistant] In fact, if all you knew was that a single, typical doctor achieves 1% error,


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[Assistant] And it might be very difficult to know if you should be trying to fit your training set even better.


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[Assistant] And this problem arose only when you're doing very well on your problem already, only when you're doing 0.7%, 0.8%, really close to human level performance. Whereas indeed, two examples on the left, when you were further away,


[User] Please transcribe this text

[Assistant] From human level performance, it was easier to tell if you should focus on bias or variance. So this is maybe an illustration of why, as you approach human level performance, it's actually harder to tease out the bias and the variance effects, and therefore why progress on your machine learning project just gets harder as you're doing really well.


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[Assistant] And so the difference between if you estimate a Bayes error tells you how much avoidable bias.


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[Assistant] Is a problem how much avoidable bias there is and the difference between trading error and def error that tells you how much.


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[Assistant] And just calling that the estimate of the bias.


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[Assistant] In contrast, in this video, we have a more nuanced analysis in which there is no particular expectation that you should get 0% error.


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[Assistant] Because sometimes Bayes Error is non-zero and sometimes it's just not possible for anything to do better than a certain threshold of error.


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[Assistant] And so in the earlier course we were measuring training error.


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[Assistant] Let's see how much bigger trading error was than zero and just using that to try to understand how big our bias is. And that turns out to work just fine for problems where Bayes' error is nearly 0%.


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[Assistant] Such as recognizing cats. Humans are near perfect for that, so Bayes' error is also near perfect for that. So that actually works okay when Bayes' error is nearly zero.


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[Assistant] But for problems where the data is noisy, like speech recognition on very noisy audio.


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[Assistant] Where it's just impossible sometimes to hear what was said and to get the correct transcription.


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[Assistant] For problems like that having a better estimate for Bayes Error can help you better estimate avoidable bias and variance and therefore make better decisions on whether to focus on bias reduction tactics or on variance reduction tactics. So to recap,


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[Assistant] Having an estimate of human level performance gives you an estimate of Bayes error and this allows you to more quickly make decisions as to whether you should focus on trying to reduce the bias or trying to reduce the variance of your algorithm.


[User] Please transcribe this text

[Assistant] And these techniques will tend to work well until you surpass human level performance, whereupon you might no longer have a good estimate of Bayes error that still helps you make this decision really clearly.


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[Assistant] Now, one of the exciting developments in deep learning has been that for more and more tasks, we're actually able to surpass human level performance. In the next video, let's talk more about the process of surpassing human level performance.


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[Assistant] Planet Earth is so spectacular, it's easy to miss the smaller things in life.


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[Assistant] Down in the leaf litter. A new one is just beginning.


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[Assistant] The jungle underworld is far too dangerous for her baby.


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[Assistant] For a frog the size of a grape, this is her Mount Everest.


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[Assistant] There may be 5,000 different kinds of little creatures living in a single tree.


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[Assistant] A bromeliad plant makes the perfect penthouse pool for her tadpole to grow in.


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[Assistant] Being so tiny makes it hard to know who's friend, foe, or food.


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[Assistant] With sharp teeth, they gouge away the bark to create miniature wells that fill up with delicious sugary sap.


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[Assistant] Good thing. There are new additions to the troop.


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[Assistant] Both parents share babysitting duties. And even the older siblings pitch in. When they're not fighting.


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[Assistant] On the forest floor, life is more of a struggle.


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[Assistant] Hardly any sunlight reaches down here, so not much can grow.


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[Assistant] But there can be too much of a good thing. Water soaked up by the plants and bark adds enormous weight to ancient trees.


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[Assistant] When a tree this size falls, it leaves a big hole in jungle life.


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[Assistant] Can strip a tree like this in a matter of days.


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[Assistant] The fallen giant attracts all sorts of other tiny leaf eaters too.


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[Assistant] Hunting among tangles of fallen branches comes easily to these miniature monkeys.


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[Assistant] After hundreds of years in shadow, suddenly, the forest floor is bathed in sunlight.


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[Assistant] Seeds that have laid dormant start small, but aim high.


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[Assistant] Soon the fallen giant is carpeted in new growth.


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[Assistant] Some have dagger-like bills. And even teeth.


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[Assistant] These flour mites have nearly drunk this heliconia dry.


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[Assistant] A Brazil nut pod is too tough for most animals.


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[Assistant] Its hunting tactics are the stuff of science fiction.


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[Assistant] Tiny creatures must be on guard at all times.


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[Assistant] The female strawberry dart frog has just laid a new clutch of eggs.


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[Assistant] Both no heavier than a sugar cube. They're evenly matched.


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[Assistant] The little frog is beginning to tire.


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[Assistant] His rival's seen off. At least for now.


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[Assistant] It's still two more weeks of guard duty before his eggs hatch.


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[Assistant] As the forest matures, more intricate relationships develop.


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[Assistant] The little bee's mission for a mate continues. But the orchid's work is done.


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[Assistant] For the pygmy marmosets, life seems to be returning to normal.


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[Assistant] When nature calls, it falls on the little guys to sort it out.


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[Assistant] This tiny superhero keeps things clean in the canopy.


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[Assistant] A beetle. Only the size of a dime. But no job is too big.


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[Assistant] And down below, one Brazil nut the agouti left behind.


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[Assistant] A strawberry dart frog, looking to find a safe place for her tiny tadpole.


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[Assistant] If something's got you down, don't worry, just ask the storybots. Just ask the storybots.


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[Assistant] Did you know that the French bullet train can go up to 584 kilometers per hour? Then it needs 16 kilometers just to stop! Next stop, Paris!


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[Assistant] Alright guys, I guess we should do it for Lylan.


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[Assistant] When stapes hits the oval window, the fluid around us moves.


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[Assistant] Did you know that a camel can drink up to 40 gallons of water at one time?


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[Assistant] If you're working on a brand new machine learning application, one of the pieces of advice I often give people is that I think you should build your first system quickly and then iterate. Let me show you what I mean.


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[Assistant] I've worked on speech recognition for many years, and if you're thinking of building a new speech recognition system, there are actually a lot of directions you could go in, a lot of things you could prioritize.


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[Assistant] For example, there are specific techniques for making speech recognition systems more robust to a noisy background. And, uh, noisy background could mean cafe noise, like a lot of people talking in the background, or car noise, the sounds of cars and highways or other types of noise.


[User] Please transcribe this text

[Assistant] There are, uh, specific problems associated with speakers that are far from the microphone. This is called far-few speech recognition. Young children's speech poses special challenges, both in terms of how they pronounce individual words as well as their choice of words and the vocabulary they tend to use. And if sometimes your speaker stutters or if they use, uh,


[User] Please transcribe this text

[Assistant] Things you could do to improve a speech recognition system.


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[Assistant] And more generally, for almost any machine learning application, there could be 50 different directions you could go in. And each of these directions is reasonable and would make your system better. But the challenge is how do you pick which of these to focus on?


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[Assistant] And even though I've worked in speech recognition for many years, if I'm building a new system for a new application domain, I would still find it maybe a little bit difficult to pick without spending some time thinking about the problem.


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[Assistant] So what do we recommend you do if you're starting on building a brand new machine learning application?


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[Assistant] Is to build your first system quickly and then iterate. What I mean by that is I recommend that you first quickly set up a dev and test set and metric. So this is really, you know, deciding where to place your target.


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[Assistant] And if you get it wrong, you can always move it later, but just set up a target somewhere.


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[Assistant] And then I recommend you build an initial machine learning system quickly. Find a training set, train it, and see, start to see and understand how well you're doing against your dev and test set in the evaluation metric.


[User] Please transcribe this text

[Assistant] When you build your initial system, you then be able to use bias-variance analysis, which we talked about earlier, as well as error analysis, which we talked about, uhm, just in the last several videos, to prioritize the next steps. In particular, if error analysis


[User] Please transcribe this text

[Assistant] Causes you to realize that a lot of the errors are from the speaker being very far from the microphone, which causes special challenges in speech recognition. Then that would give you a good reason to focus on techniques to address this, uhm, this called far-field speech recognition.


[User] Please transcribe this text

[Assistant] A lot of value of building this initial system can be a quick and dirty implementation, you know, don't overthink it.


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[Assistant] Well all the value of your initial system is having some learned system, having some trained system allows you to look at its bias and variance, to try to prioritize what to do next, allows you to do error analysis, look at some mistakes, to figure out all the different directions you could go in, which ones are actually the most worthwhile. So to recap, what I recommend you do is build your first system quickly, then iterate.


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[Assistant] This advice applies less strongly if you're working on an application area in which you have significant prior experience.


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[Assistant] It also applies a bit less strongly if there's a significant body of academic literature that you can draw on for pretty much the exact same problem you're building.


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[Assistant] So, for example, there's a large academic literature on face recognition, and if you're trying to build a face recognizer, it might be okay to build a more complex system from a get-go by building on this large body of academic literature.


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[Assistant] But if you're tackling a new problem for the first time, then I would encourage you to really not overthink, uhm, or not make your first system too complicated, but just build something quick and dirty and then use that to help you prioritize how to improve your system.


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[Assistant] So I've seen a lot of machine learning projects and I've seen some teams overthink their solution and build something too complicated. I've also seen some teams underthink it and build something maybe too simple. But on average I've seen a lot more teams overthink and build something too complicated and I've seen teams, you know, build something too simple.


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[Assistant] So uhm, I hope this helps and if you are applying to your machine learning algorithms to a new application and if your main goal is to build something that works, uhm, as opposed to if your main goal is to


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[Assistant] Use the results of those analysis to help you prioritize where to go next.


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[Assistant] And in this tutorial, I will teach you how to pass a quality assurance interview. So it does not matter what type of quality assurance role you're being interviewed for. Please do stick around and watch this from beginning to end, because it will make a big difference to your preparation. My name is Richard McMunn. As I say, welcome to this tutorial.


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[Assistant] That's me there in the center. I've been helping people for about 20 years now to pass their interviews. And I do that by giving you top scoring answers. So I'm going to do that during this video. I'm going to give you some top scoring answers to the likely QA interview questions that will appear during your interview. Please do SUBSCRIBE by clicking the red button.


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[Assistant] below the video and then you won't miss out on any of the weekly training videos I'm uploading. And I'd also very much appreciate it if you gave the video a LIKE, that always motivates me to create more content. Thank you very much. Okay.


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[Assistant] Now in preparation for your quality assurance interview, you and I need to make sure that you demonstrate the following key skills and attributes. These are that you are able to maintain the highest standards of QA at all times. That's very, very important. Also showing an ability to inspire


[User] Please transcribe this text

[Assistant] To motivate and encourage people who work within the organization to see the benefits of high quality standards.


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[Assistant] And in adversity and also making sure that you maintain those high QA standards. That's important. Also creating an organizational culture.


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[Assistant] That works towards high standards in all areas of work and also keeping and maintaining accurate records.


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[Assistant] And being organized in your role within quality assurance. So the QA, quality assurance interview questions and answers that are coming right now are perfect for any QA role, an assessor role, a manager role, an associate role. It does not matter.


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[Assistant] The following questions and answers are going to help you. Okay, let's get into question number one of your quality assurance interview.


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[Assistant] Tell me about yourself and why you will be a good fit for this quality assurance role. So I can pretty much guarantee this will be the first question during your QA interview. Here's my suggested answer.


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[Assistant] Struggle to maintain high standards, and I feel it's my job to instill quality and exceptional standards in everything my employer does.


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[Assistant] I will be particularly good in this role because I am passionate about what I do. I'm consistent in my approach to quality and I feel I have the motivational and interpersonal skills to get people to believe in the benefits of strong quality assurance standards.


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[Assistant] You will see a significant positive impact in your business. That's a great answer. It's concise. It's positive.


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[Assistant] And it shows that you have the key skills and qualities to work within this quality assurance position. Now I've got a few more questions and answers to work through. You can either take notes as I progress, or if you want to wait, I will tell you where you can download all of these and more quality assurance interview questions and answers. Question number two.


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[Assistant] Why have you chosen to work for our company in this quality assurance position? So why do you want to work for our company?


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[Assistant] I feel some organizations pay lip service to quality assurance process and they simply employ people to tick a box. I do not want to work for an organization who has that approach.


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[Assistant] Having studied your organization in detail, you not only have an outstanding reputation within the industry.


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[Assistant] But you are clearly serious about setting and maintaining high quality assurance standards. I feel that if I'm successful at interview today and you employ me within this quality assurance role


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[Assistant] Basically saying that that organization sets really high standards. They take quality assurance seriously.


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[Assistant] Which is in line with your own key skills and qualities, and that's important. That's how I would answer the question, why do you want to work for our company? Next question, number three.


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[Assistant] What skills, qualifications and attributes do you have that are a match for this quality assurance role? Now, before answering this, my advice is to read the job description for the role that you are being interviewed for. However,


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[Assistant] I've done some research for you and this is how I would personally respond to this question. Here we go.


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[Assistant] I have many years experience as a QA assessor and my track record with previous clients and employers is second to none.


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[Assistant] In respect of my skills and attributes, I set very high standards from the outset and I always brief team members


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[Assistant] Employees and departments on what the expectations are


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[Assistant] I encourage honesty and a no blame culture and will always support everyone in the organization to help them work towards and maintain exceptional quality assurance standards.


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[Assistant] So that shows you've got the right skills, the right qualities. You've got the experience to work in this quality assurance role. Now, I do have two more questions to go through, so please do stick around and watch those because they're tough questions and I've got some great answers for you. But if you click that link in the top right hand corner, it will take you through to my website where you can download my full set


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[Assistant] Quality assurance, interview questions and top scoring answers. Also, don't forget to SUBSCRIBE if you haven't done already and that way you're not going to miss out. Next question.


[User] Please transcribe this text

[Assistant] What are the most important skills to have in quality assurance and why? Here's my answer to that question. Whilst working in quality assurance, you need to have numerous skills and attributes to be successful.


[User] Please transcribe this text

[Assistant] The most important ones are planning and delivery of results. In Quality Assurance, you are ultimately responsible for delivering and maintaining the QA process consistently throughout everything you do and everything the organization does.


[User] Please transcribe this text

[Assistant] The only way you can successfully achieve these goals is to plan thoroughly and execute the delivery of your plan. If you fail in these two areas, the consequences can be far reaching.


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[Assistant] In addition to planning effectively and the thorough execution of the process, you also need to communicate clearly with everyone involved and also have outstanding motivational skills. You should be consistent in terms of standards and be prepared to stand by your position at all times.


[User] Please transcribe this text

[Assistant] You need to be a strong communicator when explaining the benefits of the QA process and also be unwavering in the face of adversity and challenges when they do arise.


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[Assistant] I believe I have all of these skills and qualities and I can be relied upon to deliver fantastic results for you in this quality assurance position. That's a great answer. Now, here's a tough question.


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[Assistant] You notice an issue in respect to quality assurance standards. A senior member of the management team says to you it can wait until Monday. What would you do? So let me repeat that question. It's Friday, 4.30pm.


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[Assistant] And everyone is ready to go home. You notice an issue in respect to quality assurance standards. A senior member of the management team says it can wait until Monday. What would you do? So this is a difficult question, but here is my model answer.


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[Assistant] First and foremost, I would be talking to a senior member of the management team, so I would be totally respectful at all times.


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[Assistant] I would speak to him or her and explain the reasons why the issue would have to be dealt with there and then, and whilst I fully understood the fact it was late on a Friday,


[User] Please transcribe this text

[Assistant] The best interests of the organization should and must come first. I would also explain that by leaving things like this until the following Monday morning, we would effectively be telling everyone who was aware of the issue that it's fine to leave it. And ultimately the QA is not as important as we might first suggest.


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[Assistant] I believe that if you hire me in this quality assurance position, I would be able to instill a culture whereby the situation we are discussing would be resolved without question late on that Friday afternoon. So that shows that you are able to stick by your position.


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[Assistant] And maintain very high standards and deal with things there and then. That's the way to answer that question. So if you want to download my full set of quality assurance interview questions and answers, click the link below the video or go to my website, Pass My Interview.com.


[User] Please transcribe this text

[Assistant] And you can have those questions and answers in your inbox within two minutes from now. Thank you very much for watching. Don't forget to SUBSCRIBE and also hit the LIKE button. Thank you very much. If you have any questions, put them in the comments section below. But other than that, I wish you all the best for passing your Quality Assurance interview. Have a brilliant day!


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[Assistant] Planet Earth is so spectacular, it's easy to miss the smaller things in life.


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[Assistant] Australia is a land full of fantastical creatures. Not just kangaroos and koalas.


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[Assistant] But even at the best of times, there are challenges.


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[Assistant] And when summer hits, and the sun grows stronger.


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[Assistant] Living on the driest inhabited continent does have some benefits. A lot of sunshine forces the gum trees to produce a lot of sugar.


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[Assistant] In spring, it makes a big part of a little sugar glider's diet.


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[Assistant] Her new baby. Just two weeks out of the pouch.


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[Assistant] He's only the length of a thumb. And completely reliant on his mother.


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[Assistant] But by midsummer, he'll need to fend for himself in a far more challenging world.


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[Assistant] To avoid injury, these lizards fight with frills.


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[Assistant] But when closely matched, it gets physical.


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[Assistant] Seen off by the dominant male. This'll have to do for now. But soon, he will need a much safer place.


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[Assistant] He can eat 60,000 of them in 10 minutes.


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[Assistant] Koalas spend 15 hours a day sleeping.


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[Assistant] It's a mad hatter pillar. Balancing.


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[Assistant] But with its crazy disguise, the Mad Hatter Pillar keeps ahead of its enemies.


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[Assistant] A clever camouflage, so she can eat without being eaten.


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[Assistant] She spends each day consuming her own body weight in leaves


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[Assistant] And there it will stay, safe underground until it hatches many months from now.


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[Assistant] Energized by the sun, the lace monitor becomes a formidable hunter.


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[Assistant] A forked tongue tastes for prey wherever it hides.


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[Assistant] With powerful hooked claws, these monster lizards even climb trees.


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[Assistant] A thorny devil. Only the length of a pencil.


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[Assistant] That allows this little devil to endure soaring temperatures.


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[Assistant] The gum tree forest provides a bit more shelter. But even here,


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[Assistant] Another male has already found a female. But he still needs to win her over.


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[Assistant] There are over 80 different kinds of peacock spiders. Each with their own unique display.


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[Assistant] Especially choreographed to the tastes of the female of that species.


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[Assistant] A male will dance like his life depends on it when courting a predatory female.


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[Assistant] Waving his legs he tests the air for female pheromones.


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[Assistant] There she is. Time to strut his stuff.


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[Assistant] At this time of year the gum leaves are covered in strange dots.


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[Assistant] Cillid bugs use their sugary sap to build shelters. No bigger than a grain of rice.


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[Assistant] As temperatures soar to 50 degrees, gum leaves wither and fall.


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[Assistant] But they're so full of toxic chemicals, they decompose very slowly.


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[Assistant] Deep leaf litter provides cover for sly predators.


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[Assistant] When this little lizard feels threatened, it goes on the offensive.


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[Assistant] The liquid is harmless, but so sticky and smelly it's enough to repel even the deadliest hunter.


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[Assistant] But when lightning strikes, all the dry leaves ignite and their volatile chemicals fuel the flames.


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[Assistant] Flames are getting higher. But the little glider is now too big to be carried. Mom's only option.


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[Assistant] As insects flee, predators move in.


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[Assistant] His first flight might be his last.


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[Assistant] echidnas can lower their body temperatures enough to survive the fires raging above.


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[Assistant] Now he must survive in a world reduced to ashes.


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[Assistant] Charred insects will see him through until life returns to the forest.


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[Assistant] Add a little water and the forest is reborn.


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[Assistant] Before long, the bush is getting back to normal.


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[Assistant] And once again, the gum trees provide sanctuary for all sorts of weird and wonderful little creatures.


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[Assistant] Over millennia, they have adapted to the annual cycle of fire.


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[Assistant] But as bushfires become bigger, hotter, and more frequent.


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[Assistant] Australia's tiny creatures will struggle to survive.


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[Assistant] For now, the young sugar glider is ready.


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[Assistant] If I'm on the bench, dead ball or something, a swing tip might pop up in my head.


[User] Please transcribe this text

[Assistant] Today we sit down with the curry clan, all of them. I'm going to ask them to introduce themselves. Okay.


[User] Please transcribe this text

[Assistant] Kia, NBA, MVP, Stephen Curry.


[User] Please transcribe this text

[Assistant] The convolution operation is one of the fundamental building blocks of a convolutional neural network. Using edge detection as the motivating example, in this video you see how the convolution operation works.


[User] Please transcribe this text

[Assistant] In previous videos I've talked about how the early layers of a neural network might detect edges and then the somewhat later layers might detect parts of objects and then even later layers maybe detect parts of complete objects like, uh, people's faces in this case.


[User] Please transcribe this text

[Assistant] In this video you see how you can detect edges in an image.


[User] Please transcribe this text

[Assistant] Let's take an example. Given a picture like that, for a computer to figure out what are the objects in this picture, the first thing you might do is maybe detect vertical edges in this image.


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[Assistant] So for example, this image has all those vertical lines where the railings are, as well as, you know, kind of vertical lines at the outline of these pedestrians. And so those get detected in this vertical edge detector output.


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[Assistant] And you might also want to detect horizontal edges. So for example, there's a very strong horizontal line where this railing is, and that also gets detected sort of roughly here.


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[Assistant] So how do you detect edges in an image like this? Let's look at an example.


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[Assistant] Here is a 6x6 grayscale image, and because this is a grayscale image, this is just a 6x6x1.


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[Assistant] Matrix rather than 6x6x3 because they're on, uh, separate RGB channels.


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[Assistant] In order to detect edges, or let's say vertical edges in this image, what you can do is construct a 3x3 matrix, and in the parlance, in the terminology of convolutional neural networks, this is going to be called a filter.


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[Assistant] And, uh, I'm gonna construct a 3x3 filter or 3x3 matrix that looks like this.


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[Assistant] Sometimes research papers would call this a kernel instead of a filter, but I'm going to use the filter terminology in these videos.


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[Assistant] And what you're gonna do is take the 6x6 image and convolve it, and the convolution operation is denoted by this asterisk.


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[Assistant] Uhm, one slightly unfortunate thing about the notation is that in mathematics, the asterisk is the standard symbol for convolution, but in Python, this is also used to denote multiplication or maybe element-wise multiplication.


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[Assistant] So this asterisk has dual purposes, it's overloaded notation, but I'll try to be clear in these videos when this asterisk refers to convolution.


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[Assistant] And the output of this convolution operator will be a 4 by 4.


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[Assistant] Matrix, which you can interpret, which you can think of as a 4x4 image.


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[Assistant] And the way you compute this 4x4 output is as follows. To compute the first element, the upper left element of this 4x4 matrix, what you're going to do is take the 3x3 filter and paste it on top of the 3x3 region of your original input image.


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[Assistant] And what you should do is take the element-wise product, so the first one would be 3 times 1.


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[Assistant] And then the second one would be 1 times 1, I'm going down here, 1 times 1, and then plus, uhm, 2 times 1.


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[Assistant] This one. And then add up all of the resulting 9 numbers. So then the middle column gives you 0 times 0, plus 5 times 0, plus 7 times 0. And then the rightmost column gives 1 times minus 1, 8 times minus 1, plus 2 times minus 1. Uhm, and adding up these 9 numbers will give you negative 5. And so I'm gonna fill in negative 5 over here.


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[Assistant] Okay, and you can add up these nine numbers in any order of course. It's just that I went down the first row, then it's just that I went down the first column, then the second column, then the third.


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[Assistant] Next, to figure out what is this second element, you're gonna take the


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[Assistant] Blue square and shift it one step to the right. Like so.


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[Assistant] And let me get rid of the green marks here. And you're going to do the same element-wise product and then addition. So you have zero times one.


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[Assistant] Plus five times one, plus seven times one, plus one times zero, plus eight times zero, plus two times zero.


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[Assistant] Plus 2 times negative 1 plus 9 times negative 1 plus 5 times negative 1.


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[Assistant] And if you add up those nine numbers, you end up with negative four.


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[Assistant] And so on. If you shift this to the right, do the nine products and add them up, you get zero. And then,


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[Assistant] Over here, you should get 8. And just to verify, you know, you have...


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[Assistant] Uh, 2 plus 9 plus 5, that's 16. Then the middle column gives you 0. And then the rightmost column,


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[Assistant] 4 plus 1 plus V, uh, times negative 1, that's minus 8. So that's 16 on the left column, minus 8, and that gives you 8, right, like we have over here. Next, in order to get you this element,


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[Assistant] In the next row, what you do is take the blue square and now shift it one down. So you now have it in that position. And again, repeat the element-wise product and then adding exercise. And if you do that, you should get negative 10 here.


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[Assistant] To then fill in all the rest of the elements of the matrix. And so to be clear, this minus 16 would be obtained by, you know, from this, uh, lower right 3 by 3 region.


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[Assistant] So a 6x6 matrix convolved with a 3x3 matrix gives you a 4x4 matrix.


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[Assistant] And these are images and filters. These are really just matrices of various dimensions.


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[Assistant] But the matrix on the left is convenient to interpret as a image.


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[Assistant] Uhm, and the one in the middle we interpret as a filter and the one on the right we can interpret that as maybe another image.


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[Assistant] And this turns out to be a vertical edge detector. And you'll see why on the next slide.


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[Assistant] Before going on though, just one other comment, which is that if you implement this in a programming language, then in practice, uh, most programming languages will have some different function rather than an asterisk to denote convolution. So for example, in the programming exercise, you, uhm, usually implement a function called con forward


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[Assistant] Conf2D, you know, and then other deep learning programming frameworks in the Keras programming framework, which you see later in this course, there's a function called Conf2D that implements convolution and so on.


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[Assistant] But all the deep learning frameworks that have a good support for computer vision will have, you know, some function for implementing this convolution operator.


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[Assistant] So why is this doing vertical edge detection? Let's look at another example.


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[Assistant] So the illustrator is, we're going to use a simplified image. So here is a simple 6 by 6 image where the left half of the image is 10 and the right half is 0.


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[Assistant] If you thought this is a picture, it might look like this, where the left half, the tens, give you brighter pixel intensity values, and the right half gives you darker pixel intensity values. I'm using that shade of grey, uh, to denote zeros, although maybe it could also be, uh, drawn as black.


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[Assistant] But in this image, there's clearly a very strong vertical edge right down the middle of this image as it transitions from white to black or white to a darker color.


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[Assistant] So, when you convolve this with the 3x3 filter,


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[Assistant] And so this 3x3 filter can be visualized as follows, where it is lighter.


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[Assistant] Brighter pixels on the left, and then there's midtones, zeros in the middle, and then darker on the right. What you get is this matrix on the right.


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[Assistant] So just to verify this math that you want, uhm, this zero, for example, is obtained by


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[Assistant] Taking the element-wise products and then multiplying with this 3x3 block and so you get from the left column


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[Assistant] Uh, 10 plus 10 plus 10, and then zeros in the middle, and then minus 10 minus 10 minus 10, which is why you end up with zero over here.


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[Assistant] Whereas in contrast if, uh, that 30 would be obtained from this.


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[Assistant] Which you get from having, you know, 10 plus 10 plus 10 and then minus zero.


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[Assistant] Minus zero, which is why you end up with a thirty over there.


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[Assistant] Now if you plot this rightmost matrix as an image, it will look like that, where there's this lighter region right in the middle. And that corresponds to, is having detected this vertical edge down the middle of your 6x6 image.


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[Assistant] And in case the dimensions here seem a little bit wrong, you know, that the detected edge seems really thick, that's only because we're working with very small images in this example. And if you're using, say, a thousand by thousand image rather than a six by six image, then, uhm, you find that this


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[Assistant] Uh, you know, does a pretty good job, really, detecting the edges, the vertical edges in your image. And in this example, this, uhm, right region in the middle is just the output image's way of saying that it looks like there's a strong vertical edge right down the middle of the image.


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[Assistant] And maybe one intuition to take away from vertical edge detection is that a vertical edge is a 3x3 region, since we're using a 3x3 filter, where there are bright pixels on the left, uhm, and you don't care that much what's in the middle, and dark pixels on the right.


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[Assistant] And the middle of the image is really, uh, in the 6x6 image is really where, you know, there could be bright pixels on the left.


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[Assistant] And darker pixels on the right, that's why it thinks there's a vertical edge over there.


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[Assistant] And convolution operation gives you a convenient way to specify how to find these vertical edges in an image.


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[Assistant] So you've now seen how the convolution operator works. In the next video, you'll see how to take this and use it as one of the basic building blocks of a convolutional neural network.


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[Assistant] Alright, alright, that helped a lot actually. Alright, here we go.


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[Assistant] I wonder where the road that is scattered and scattered is?


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[Assistant] Kigatsukeba kareyuku eda ni hitori nokosareteta


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[Assistant] If something's got your back on worrying on, just ask the storybots. Just ask the storybots.


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[Assistant] If something's got your back on, worry not. Just ask the storybots. Just ask the storybots.


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[Assistant] From there it's off to a switching station, which, this is wild, sends those waves to the one exact individual phone you dialed.


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[Assistant] Dreams of finding lost treasure almost came true recently. A new machine called the Revealer has been invented.


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[Assistant] And it has been used to detect gold which has been buried in the ground. The machine was used in a cave near the seashore where it is said pirates used to hide gold.


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[Assistant] The pirates would often bury gold in the cave and then fail to collect it. Armed with the new machine


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[Assistant] A search party went into the cave, hoping to find buried treasure.


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[Assistant] The leader of the party was examining the soil near the entrance to the cave


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[Assistant] When the machine showed that there was gold under the ground. Very excited, the party dug a hole two feet deep. They finally found a small gold coin, which was almost worthless.


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[Assistant] May reveal something of value fairly soon.


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[Assistant] Welcome to this section in learning Elasticsearch 5.0 entitled Advanced Elasticsearch Queries. In this section,


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[Assistant] We look at some advanced queries and techniques in Elasticsearch. We start with sorting.


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[Assistant] The section is then concluded with a dive into synonyms, a key element to optimizing search query results. In this particular video, we look at sorting in Elasticsearch. More specifically, we look at the what's and the why's of sorting. We then go into how sorting is achieved in Elasticsearch


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[Assistant] Sorting an Elasticsearch allows for the ordering of query results per fields in ascending or descending order. So why sort?


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[Assistant] We can find the least recent date for documents in an index.


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[Assistant] Note, these are but a small sample size of possible reasons to sort. Going back to our books index, this is an excellent example of sorting.


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[Assistant] First, notice the query body is essentially bisected into two parts. A, the sorting parameters, and B,


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[Assistant] In this query we search for all the books that contain big data in their tag field


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[Assistant] In the case of fields that contain say an array value of numbers, the use of sort mode would help tremendously.


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[Assistant] Let's imagine we had a field which contained the different purchase prices paid for a given book in our index. A match all query using the sort mode of average would produce an ordered list of the average price paid for each book. Let's take a look.


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[Assistant] This example assumes that we have a price paid field with an array list of prices.


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[Assistant] Note that we could have just as well replaced the average mode with the max to get the maximum values from each array.


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[Assistant] As we've seen in previous examples of aggregation queries, the missing parameter also applies to sorting.


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[Assistant] As such, you can use missing to set the value of documents that don't have the specific field. Can values include underscore first and underscore last where the field value is set to the first or last value of the specified fields respectively. You also have the option of setting a custom value here.


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[Assistant] Here we're in Kibana, we're going to look at an example of sorting in Elasticsearch. So let me paste the code here. And what we're going to do is we're going to sort through our books index.


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[Assistant] And, uh, we're going to do it in ascending order and it's based on the timestamp field, of course, pub date.


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[Assistant] We're going to do a match all query, so we want to get all the books ordered. Let's see what happens.


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[Assistant] So here we have the results. If we look at our pub date.


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[Assistant] So it placed it in ascending order. If we change this to descending.


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[Assistant] Also notice the use of pagination parameters, from and to. Remember that?


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[Assistant] Here, knowing that we had less than 30 documents in our books index, I wanted to list all of them. So it was kind of a shortcut. I just decided to set the from value to zero and the to value to 30. Otherwise elastic search will limit the output to around 10. And that concludes this video. Up next, we look at geo searching.


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[Assistant] As you can see, a single pitch all by itself can only take you so far.


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[Assistant] What it's going to take for us is a unified sense of purpose and spirit. Players have to be connected. Nothing can get in the way of that.


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[Assistant] We've been up and down this year with our chemistry, uhm, had a lot of adversity, a lot of bumps in the road.


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[Assistant] X's and O's are maybe 20% of the job. You know, the, uh, the other stuff is about 80, 80 to 90, maybe it's 90.


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[Assistant] Didn't help matters, but when we returned to the bay, uhm, you know, he was an integral part of trying to repair our spiritual injury.


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[Assistant] Build bridges and bring people together who have the respect of everybody. And that's what Steph represents for us.


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[Assistant] If something's got your back on, worry not. Just ask the storybots. Just ask the storybots.


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[Assistant] Hi there, my name's Richard McMunn from the interview training website, PassMyInterview.com. And in a second, I'm going to walk through this door and undertake a live supervisor interview.


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[Assistant] So if you have a supervisor interview coming up for any organization whatsoever, make sure you stay tuned because I will help you to pass it. So let's get straight into the interview and I'll teach you what to say in your supervisor interview. Let's go.


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[Assistant] Well, thank you very much for inviting me to be interviewed for this supervisor position today. Prior to applying for the role.


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[Assistant] I studied the job description in detail to make sure I had the necessary skills, the qualities and the attributes to come into the position and make a fast and immediate impact to your organization. Over the years, I've worked in various positions where I have built up lots of experience and transferable qualities


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[Assistant] I am a very good communicator. I always make sure that I communicate to people in a concise manner. I always make sure that I'm good at building relationships with people and I understand


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[Assistant] I'm also a very good problem solver. So I have an innovative approach to solving problems. I am a creative problem solver. So when problems occur within a team, I will be that person who will take control


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[Assistant] Will prove to you that I am the right person for this supervisor role. For example, in my last position, I worked as part of a team


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[Assistant] To help increase sales within the organization. And we did that by getting together, brainstorming ideas and coming up with different marketing methods to help the organization increase sales. We actually increased sales by 25% from the previous year.


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[Assistant] I am the type of person who will quickly come into this role and you will get


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[Assistant] A positive return on your investment because I will always focus everything that I do


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[Assistant] First and foremost, I now believe that I have the necessary skills and qualities required to come into the position and do a really good job.


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[Assistant] There comes a lot of responsibility with the position of supervisor, and I believe I am now ready


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[Assistant] Other supervisors and managers to see how they motivate their teams and how they achieve.


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[Assistant] I feel therefore that I have a natural affinity to the role of a supervisor. I also want to be a supervisor because I'm looking to have a position within an organization


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[Assistant] In a previous position, my manager knew that I wanted to be a supervisor and she let me look after


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[Assistant] Who was able to come quickly, bring in the goods that we needed, and we were then able to get them out to our customers on time. So I already have the ability to work under pressure and work at pace to overcome difficult and challenging problems. So those are the reasons why I want to become a supervisor.


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[Assistant] There are various skills and qualities needed to be a competent supervisor. You need to be a really good communicator. So when you're communicating with your team,


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[Assistant] Whilst liaising with external stakeholders, contractors and suppliers because you need them


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[Assistant] And external stakeholders and suppliers. You should also be really good at conflict resolution as a supervisor. That's a really important skill in my opinion.


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[Assistant] I've carried out a little bit of research around those. Your customers and clients obviously love everything that you do here. And that tells me that you are at the cutting edge of what customers and clients want. You are innovative and you are creative. And again, being a creative person myself, I will feel that I can come into the position and I will be given the freedom


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[Assistant] Whenever conflict occurred within a team that I was supervising, I would


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[Assistant] Expect the team members to initially try to resolve the conflict themselves.


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[Assistant] Because after all, they are mature and professional adults, as I mentioned earlier, and they should be able to resolve the conflict themselves and also realize


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[Assistant] The objectives of our team and the organization must come above everything else. So they should resolve it themselves.


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[Assistant] I will lay out my standards and expectations and I will say to them, if you encounter conflict, you should be able to resolve it yourselves initially. I will then observe


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[Assistant] And confident supervisor who's not afraid to make difficult and unpopular decisions at times that will help the organization to drive forward and achieve its goals.


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[Assistant] And I have the skills and expertise and the experience to make the right decisions for your organization. I am also a transformational style


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[Assistant] And if I am successful in this role and I get to become your supervisor, I will be sitting down with my team members. I will be encouraging them to achieve


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[Assistant] During team meetings, whilst we were discussing ideas to achieve a particular project or task, he would be quite dismissive and sometimes I would suggest obstructive.


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[Assistant] And what he'd done during his time with the organization. I got him to talk about the things that he enjoyed doing and about how he had contributed to the team.


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[Assistant] And make him feel proud to achieve the goal right at the end of the project.


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[Assistant] So I used his strengths to my advantage and I kept in contact with him throughout the duration of that project. And I built up a really good relationship with him. I found that by empowering him and using his strengths, I was able to get him again to be a high performing member of the team. So whenever I encounter


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[Assistant] Give them tasks and projects that help to develop their weak areas so you can help them to constantly improve and develop. And that's good for them. That will make them feel good as a member of the organization. I would find out what motivates each individual member of my team, because when you find out what motivates them,


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[Assistant] Then that can be demotivating within a team. So I would always praise them.


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[Assistant] But conversely, if they don't do things right, then I would be honest with them and I would tell them, because that's also important when you are motivating your team and helping them to move forward and achieve the organization's goals.


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[Assistant] So the next thing to do if you want to pass your supervisor interview is to make sure you CLICK THAT LINK RIGHT NOW in the top right hand corner of the video and it will take you through


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[Assistant] I've put my LinkedIn link in the description below the video.


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[Assistant] That tells me that you find the content useful and it motivates me to create more videos for you. Thank you so much for watching and I wish you all the best for passing your supervisor interview. Have a brilliant day!


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[Assistant] Only one reason why he didn't get recruited by major D1s, the Carolinas, the Dukes, even the Virginia Techs, uhm, is because he was a small guy who didn't pass the eye test.


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[Assistant] One, he bought my wife over by telling her that. She'd take his son just like he is. But also showed he had great confidence in the ability of Steph, and he knew he could improve him as a player.


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[Assistant] People tell you, no, you can't, you're not good enough to play for me. Obviously any competitive guy is going to remember that and it's going to motivate them.


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[Assistant] Steph, I'll probably tell you, if he had to do it all over again, and he knew what he'd do now, he'd still go to Dave's.


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[Assistant] Tonight, you know, there's a lot on the line. You know, the family bet is that, you know, whoever loses has to buy the tickets for the family for the rest of their career.


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[Assistant] Legend tells of a time in ages past.


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[Assistant] Home. And so it was just really nice to be there and kind of have a nostalgic moment seeing where we started off and where we are now.


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[Assistant] PassMyInterview.com. And in this tutorial, I will teach you how to pass a supply chain interview. So it does not matter whether you are being interviewed to become a supply chain manager.


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[Assistant] Um, a supply chain specialist, or even if you are applying to be part of a graduate scheme within supply chain, this tutorial will make a huge difference to your preparation. So please do stick around and watch it from beginning to end. Now, before I get into those tips and the all important


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[Assistant] Supply Chain Interview Questions and Answers. A very warm welcome to this tutorial. As I say, my name is Richard McMunn. That's me there in the center, and I'm going to focus on giving you top scoring answers that you can't find anywhere else. On that basis, please do watch the video from beginning to end. Please also SUBSCRIBE, and I would very much appreciate it if you gave the video a LIKE


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[Assistant] That always motivates me to create more content for you. Thank you very much. Now in preparation for your supply chain interview, you and I need to make sure


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[Assistant] You demonstrate the following key skills and attributes. These are the things that you will be assessed against. Your ability to build positive, long lasting relationships with key suppliers, manufacturers and also logistic companies. You also should have a thorough understanding


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[Assistant] Believe they have the perfect answer to this, but it can catch you out because it's a simple question, but you need to make sure you demonstrate you have a thorough understanding. And I will make sure that you do that during this tutorial. Also demonstrate that you have a determined


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[Assistant] Prove that you have accuracy skills, problem solving skills, and also an ability to solve problems whilst you are under pressure. So with the following supply chain interview questions and answers that are coming right now, I will make sure that you demonstrate all of those key


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[Assistant] So the first question during any Supply Chain interview is going to be, tell me about yourself.


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[Assistant] So this question I have created for you is tell me about yourself and why we should hire you in this supply chain position. So here is my perfect answer to this question.


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[Assistant] I have worked in supply chain for a number of years now and I feel the experiences I have gained.


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[Assistant] Have brought me to the position I am in today where I am the perfect match for your company.


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[Assistant] I am conscientious, determined, resilient and professional in my approach to my supply chain duties. And I also understand that without someone performing their duties in this position to a high standard, your organization cannot function effectively.


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[Assistant] With me, you will get someone who has a thorough understanding of the supply chain process, an ability to work effectively as part of a team, and also a proven track record of achievement whilst completing difficult tasks under pressure.


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[Assistant] You should hire me simply because based on my experience, skills and my passion, I am the best person for this job. So that is a confident answer. It's a positive answer. And it also demonstrates the core skills that are required to be competent within any supply chain position. So you can even take a note.


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[Assistant] Of these answers, you can pause the video and write them down. Or if you stick around, I will tell you where you can download my full set of 22 supply chain interview questions and answers as we progress. So second question, what is the supply chain?


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[Assistant] Believe they know what it is, but when they come to answer it, they can get caught out.