[User] Please transcribe this text

[Assistant] And then, uhm, the backward function using the backprop step will be another function that now inputs dA of L and outputs dA L minus 1. So it tells you


[User] Please transcribe this text

[Assistant] Given the derivatives respect to these activations, that's DAFL, what are the derivatives? How much do I wish?


[User] Please transcribe this text

[Assistant] You know, AL minus 1 changes the computer derivatives with respect to deactivations from the previous layer.


[User] Please transcribe this text

[Assistant] Uhm, within this box, right, you need to use wl and bl, and it turns out along the way you end up computing dzl, uhm, and then this box, this backward function can also output dwl and dbl.


[User] Please transcribe this text

[Assistant] But I was sometimes using red arrows to denote the backward iterations, so if you prefer we could draw these arrows in red. So if you can implement


[User] Please transcribe this text

[Assistant] These two functions, then the basic computation of the neural network will be as follows. You're going to take the input features a0, feed that in,


[User] Please transcribe this text

[Assistant] And that will compute the activations of the first layer, let's call that A1. And to do that you need W1 and B1. Uhm, and then we'll also, you know, cache away Z1. Now having done that, you feed that to the second layer and then using W2 and B2, you're going to compute the activations of the next layer, A2, and so on.


[User] Please transcribe this text

[Assistant] Until eventually, you end up outputting a capital L, which is equal to y hat. And along the way, we cached all of these values z.


[User] Please transcribe this text

[Assistant] So that's the forward propagation step. Now for the back propagation step, what we're going to do will be a backward sequence of iterations in which you are


[User] Please transcribe this text

[Assistant] Going backwards and computing gradients like so.


[User] Please transcribe this text

[Assistant] So we're actually going to feed in here dA of L, and then this box will give us dA of L minus 1, and so on, until we get dA of 2, dA of 1,


[User] Please transcribe this text

[Assistant] Uhm, you could actually get one more output to compute DA0.


[User] Please transcribe this text

[Assistant] But this is derivative with respect to your input features, which is not useful, at least for...


[User] Please transcribe this text

[Assistant] Trading the weights of these supervised neural networks, uhm, so you could just stop it there. But along the way, BACPROP also ends up outputting DWL, DBL, right, this used the parameters WL and BL, uhm, this would output DW3, DB3, and so on.


[User] Please transcribe this text

[Assistant] Uhm, and so just to maybe fill in the structure of this a little bit more, right, these boxes will use those parameters as well. Uhm, wl, bl.


[User] Please transcribe this text

[Assistant] And it turns out that we'll see later that inside these boxes we'll end up computing these z's as well.


[User] Please transcribe this text

[Assistant] So one iteration of training for your neural network involves starting with A0, which is X, and going through forward prop as follows.


[User] Please transcribe this text

[Assistant] Uhm, computing y hat and then using that to compute this and then back prop, right, doing that. And now you have all these derivative terms and so


[User] Please transcribe this text

[Assistant] You know, w will get updated as w minus the learning rate times dw, right, for each of the layers. And similarly, uhm, for b, right. Now that you've computed the back prop and have all these derivatives.


[User] Please transcribe this text

[Assistant] So that's one iteration of gradient descent for your neural network. Now before moving on, just one more implementational detail.


[User] Please transcribe this text

[Assistant] Conceptually, it would be useful to think of the cashier as storing the value of z.


[User] Please transcribe this text

[Assistant] Uhm, for the backward functions. But when you implement this, and you see this in the programming exercise, when you implement this, you find that the cache may be a convenient way to get this value of the parameters of w1, b1 into the backward function as well. So in the programming exercise, you, you actually store in the cache z as well as w and b. Alright, so to store z2, w2, b2,


[User] Please transcribe this text

[Assistant] But from an implementational standpoint, I just find this a convenient way to just, you know, get the parameters copied to where you need them.


[User] Please transcribe this text

[Assistant] You need to use them later when you're computing backpropagation. So that's just an implementational detail that you see when you, uhm, do the programming exercise.


[User] Please transcribe this text

[Assistant] So you've now seen one of the basic building blocks for implementing a deep neural network. In each layer there's a forward propagation step and there's a corresponding backward propagation step.


[User] Please transcribe this text

[Assistant] And does it catch the positive information from one to the other?


[User] Please transcribe this text

[Assistant] In the next video, we'll talk about how you can actually implement these building blocks. Let's go into the next video.


[User] Please transcribe this text

[Assistant] We'll produce at a lower cost, so firm B will produce less because they know that we'll get most of the...


[User] Please transcribe this text

[Assistant] Now, this view is known in the philosophy of science literature as scientific realism.


[User] Please transcribe this text

[Assistant] Scientific realism says that the aim of science is to provide us with theories which, literally construed, we believe to be true. There are two different aspects in this definition.


[User] Please transcribe this text

[Assistant] And he basically says that we should take the language of science at face value. So if we have a theory, say Copernican astronomy, that talks about planets,


[User] Please transcribe this text

[Assistant] We believe that the theory is true or at least approximately true. In other words,


[User] Please transcribe this text

[Assistant] So we need a proper philosophical argument for scientific realism.


[User] Please transcribe this text

[Assistant] So in the original formulation of Hilary Putnam, the No Miracles Argument says that scientific realism is the only philosophy that does not make the success of science a miracle. The terms refer


[User] Please transcribe this text

[Assistant] If those objects don't exist, if our theories were not really true, or at least approximately true, of the same objects, then it would be just a miracle


[User] Please transcribe this text

[Assistant] That we have a theory like Newtonian mechanics that talks about gravitational attraction and is so successful in predicting phenomena.


[User] Please transcribe this text

[Assistant] One must be a scientific realistic in order to explain


[User] Please transcribe this text

[Assistant] In this section, we are going to take a look at all the products of the Elastic Family, followed by the importance of analyzing your data with Elastic Stack. Finally, we will be installing and starting Elastic Search, Logstash, and Kibana in a system running with Windows operating system. Chapter 1. The Elastic Family.


[User] Please transcribe this text

[Assistant] In this video, we are going to take a look at a brief introduction about all the product offerings of Elastic and how each product fit into the stack.


[User] Please transcribe this text

[Assistant] The first and most important product of the Elastic Stack family is Elasticsearch, a distributed, full-text search server and analytic engine that is built on top of Apache Lucid. It serves as the heart of the Elastic Stack.


[User] Please transcribe this text

[Assistant] Elasticsearch was first developed by Shea Bannon and was open sourced as a distributed search engine in 2010.


[User] Please transcribe this text

[Assistant] Right from its first release, it gained popularity for its ease of use and was implemented by many companies. Later, in 2012, the Elasticsearch company was founded by the people behind Elasticsearch and Apache Lucid.


[User] Please transcribe this text

[Assistant] To better align the company with the broad solution it provided


[User] Please transcribe this text

[Assistant] The next product in the Elastic Stack family is Lockstash.


[User] Please transcribe this text

[Assistant] An open source data collection engine with real-time pipelining capabilities. It was started by Jordan Cecil in 2009. Later in August 2013, Logstash was added to the Elastic family. A few months later, a new product by the name Logstash Forwarder was introduced.


[User] Please transcribe this text

[Assistant] The main difference between logstash and logstash forwarder was that


[User] Please transcribe this text

[Assistant] Logstash forwarder was used only to ship logs from server, whereas Logstash had capabilities of converting the files to JSON document and had the ability to send to various output nodes like Kafka, File, etc.


[User] Please transcribe this text

[Assistant] The other reason being for the existence of Logstash forwarder was that Logstash is a Java based program and it was heavy to install on all the host servers. In contrary, Logstash forwarder was a lightweight product and was used to collect log files from all the servers. The next member in the product line is Kibana.


[User] Please transcribe this text

[Assistant] An open source, browser based, analytic and search dashboard for Elasticsearch. It was created by Rashid Khan in 2011. Later, in Jan 2013, Rashid joined the Elasticsearch team


[User] Please transcribe this text

[Assistant] And it became the visual front end of the elastic stack.


[User] Please transcribe this text

[Assistant] Together these three products became a very powerful tool called the ELK stack or the ELP stack.


[User] Please transcribe this text

[Assistant] are a few examples among a large number of companies who were benefited by the ELK stack.


[User] Please transcribe this text

[Assistant] In May 2015, Elastic introduced the Beats family, a platform for building lightweight open source data shippers that can send data to Elasticsearch which can be analyzed later. We will be seeing in detail about the Beats platform in the upcoming slides.


[User] Please transcribe this text

[Assistant] Elasticsearch, Logstash, Kibana and Beats. A new product by the name xpack was introduced in Feb 2016 that extends the Elastic Stack with features such as security, alerting, monitoring, reporting and graph.


[User] Please transcribe this text

[Assistant] We will look into each product of XPAC later in this video. The Beats platform.


[User] Please transcribe this text

[Assistant] A real-time network and packet analytics solution was developed by Tudor and Monika and was acquired by Elastic in May 2015. Along with this, they also unveiled the Beats platform.


[User] Please transcribe this text

[Assistant] In the same release, another Beats was also introduced by the name File Beats, which was the replacement of LogStash forwarder.


[User] Please transcribe this text

[Assistant] Metric Beats. Metric Beats is a lightweight shipper that can be installed on servers to periodically collect metrics from the operating system and the services running on it. Previously, the product was known by the name Top Beats.


[User] Please transcribe this text

[Assistant] In Feb 2016, Windlock Beat, a lightweight shipper


[User] Please transcribe this text

[Assistant] Windows Event Logs was introduced. It runs as a Windows service and can ship event log data to Elasticsearch or Logstash. Later, in July 2016, the LibBeat framework was unveiled through which the developers can develop their own beats.


[User] Please transcribe this text

[Assistant] Some of the famous community beats are ping beats and docker beats. XPAC. Monitoring.


[User] Please transcribe this text

[Assistant] In Jan 2014, Elastic introduced their first commercial product by the name Marvel, a product which will monitor Elastic search deployments in real time.


[User] Please transcribe this text

[Assistant] Giving system administrators complete transparency into the state of their deployments. As part of the xpack package, it was later renamed to monitoring. Shield.


[User] Please transcribe this text

[Assistant] In Jan 2015, Elastic introduced a commercial plugin by the name Shield to enhance the security and administrative functionalities in Elasticsearch. A few features of Shield are providing role-based access control, LDAP-based authentication system support, and audit logging.


[User] Please transcribe this text

[Assistant] Watcher allowed companies that use Elasticsearch for real time search and analytics to set up alerts and notifications around the changes and trends in their data. Reporting.


[User] Please transcribe this text

[Assistant] A new future called reporting was introduced with XPAC, which was used to generate scheduled and email dashboard reporting. A new future called reporting was introduced along with the XPAC package.


[User] Please transcribe this text

[Assistant] Reporting was used to generate, schedule an email dashboard as PDF reports to any user or group to collaborate among the team.


[User] Please transcribe this text

[Assistant] In March 2016, Graph, a new extension of Elasticsearch and Kibana, was introduced which allows users to uncover, understand and explore the relationship between the data.


[User] Please transcribe this text

[Assistant] In the next video, we will be seeing how to make sense of your data using the Elastic Stack.


[User] Please transcribe this text

[Assistant] Rollin' along, rollin' along, rollin' along and singin' a song. Open roads where I belong, rollin' along, rollin' along.


[User] Please transcribe this text

[Assistant] In celebration of 420, we're gonna take a look at the cadavers and see if we can figure out what's going on with some of the common side effects of cannabis consumption, such as dry mouth, red eyes, and of course, the munchies. So, let's do this.


[User] Please transcribe this text

[Assistant] First thing you need to understand is that THC and CBD are examples of what are called cannabinoids.


[User] Please transcribe this text

[Assistant] Or is it cannabinoids? I still don't know how to properly say that, but regardless, these cannabinoids are found inside of cannabis. But there are over 100 known cannabinoids, and THC and CBD are just two of them. THC being the psychoactive one, CBD being the non-psychoactive one. Now, you have to understand that these


[User] Please transcribe this text

[Assistant] Cannabinoids are going to bind to receptors on the outside of cells called cannabinoid receptors.


[User] Please transcribe this text

[Assistant] And they are part of this larger system called the endocannabinoid system. And there's still so much we need to learn about this endocannabinoid system. It's ridiculous. But what that means is when you consume THC or cannabis products, it's not just going to go to your brain. It's going to start interacting with the tissues all over the body.


[User] Please transcribe this text

[Assistant] And one of the first tissues it's going to interact with is your heart.


[User] Please transcribe this text

[Assistant] So we have a heart here and you're looking at an anterior view. This is going to be the bottom of the heart. This is the top of the heart. And you can also see


[User] Please transcribe this text

[Assistant] We've made some incisions so we can see inside of the heart itself.


[User] Please transcribe this text

[Assistant] But what's going to happen is THC is going to start accelerating the heart rate when you first consume it. And as that happens, you are going to get an increase in blood pressure.


[User] Please transcribe this text

[Assistant] So this blood vessel right here is the ascending aorta. This is the largest blood vessel in the body. I mean, it's the size of a garden hose. It's ridiculous, but you're going to get an increase in blood pressure.


[User] Please transcribe this text

[Assistant] But after, I don't know, like 5, 10, 15 minutes or so, the heart rate is actually going to lower.


[User] Please transcribe this text

[Assistant] And that is going to give you lower blood pressure. And the thing is


[User] Please transcribe this text

[Assistant] When you have lower blood pressure, that means less blood is getting to certain areas of the body. And if that happens in the eyes, well,


[User] Please transcribe this text

[Assistant] The eyes have a ridiculous amount of these CB receptors, these cannabinoid receptors.


[User] Please transcribe this text

[Assistant] But also the blood vessels inside of the eyes also have a bunch of these cannabinoid receptors on them. So THC will also bind to them. And when you have that lower blood pressure,


[User] Please transcribe this text

[Assistant] What'll happen is the capillaries are gonna start to dilate. By dilating, more blood is able to get inside.


[User] Please transcribe this text

[Assistant] But what that also means is it starts to kind of like, pool. Cause you're actually, cause again, you have low blood pressure. So as it's pooling, and there's more of it there, the eyes start to become red. And that's why you don't get these red eyes right off the bat. It's not like you, you take your hit and then


[User] Please transcribe this text

[Assistant] You know, your eyes are ridiculously red. It takes a little bit of time to actually...


[User] Please transcribe this text

[Assistant] Happen. Another common side effect of cannabis use is going to be dry mouth. Now in order to understand that, we gotta figure out, well, what lubricates the mouth in the first place. So...


[User] Please transcribe this text

[Assistant] But if I pull this here, you're looking at a portion of the tongue that's been cut.


[User] Please transcribe this text

[Assistant] And we are now moving into the voice box. Well, it's real name is the larynx. And just below it, we have this purple gland here called the thyroid.


[User] Please transcribe this text

[Assistant] And then hiding behind it is this long tube here known as the esophagus. And we can even see a nerve dangling back here. But what we want to focus on is this thing right here. So what I'm holding is a salivary gland.


[User] Please transcribe this text

[Assistant] Specifically, this is called the submandibular salivary gland. And as its name suggests, it's going to produce saliva. So you actually have six total salivary glands. You have two by the ear.


[User] Please transcribe this text

[Assistant] Two right here around the jawline and then two underneath your tongue. Well, what they do is they secrete saliva into your oral cavity or your mouth.


[User] Please transcribe this text

[Assistant] Has a ton of cannabinoid receptors on it.


[User] Please transcribe this text

[Assistant] down, down regulate its production of saliva. So as you're not secreting saliva and if I


[User] Please transcribe this text

[Assistant] Hopefully you can see this. Do my best. Yeah, you can see there's some ducts there. So what'll happen is you're not actually transporting saliva through those ducts into the oral cavity and what you end up with is dry mouth.


[User] Please transcribe this text

[Assistant] So here's the thing, we used to think for a long time that both dry mouth and red eyes were a product of smoke itself.


[User] Please transcribe this text

[Assistant] I mean that kind of makes sense if you think about it. The smoke is flowing up and floating up into the eyes. It's going throughout the mouth. It's hot. It's dry. That makes sense that that would be the case.


[User] Please transcribe this text

[Assistant] Once we started consuming cannabis products that weren't smoke related, so like edibles for example,


[User] Please transcribe this text

[Assistant] There really is so much still to learn about the endocannabinoid system, but it is cool to see that it's not just about irritating it with products like smoke, it's literally just the cannabinoids themselves.


[User] Please transcribe this text

[Assistant] But, we now need to talk about the most famous of probably all the side effects, and that's everyone's favorite, the munchies.


[User] Please transcribe this text

[Assistant] So you're looking at a bisected head that's been cut in the mid-sagittal plane, which allows us to see so many different structures like the nasal septum, this really cool piece of tissue here called the falx cerebri.


[User] Please transcribe this text

[Assistant] The corpus callosum, uh, the brainstem, and the cerebellum back here is just stunning to look at, but we want to focus right here.


[User] Please transcribe this text

[Assistant] This area that's not really all that noticeable that I'm tracing with my probe is what's called the hypothalamus. And this hypothalamus is extremely important because you can kind of think of it like a thermostat of sorts and what it's doing is it's monitoring


[User] Please transcribe this text

[Assistant] Many different areas of the body to make sure they don't go too, like they're not functioning at too high of a level or too low of a levels. It's like, it's literally bringing things back into balance.


[User] Please transcribe this text

[Assistant] Now, inside of the hypothalamus, if we go back here,


[User] Please transcribe this text

[Assistant] Inside of that hypothalamus are these tiny little neurons, and by tiny I mean microscopic, so I can't show you right here.


[User] Please transcribe this text

[Assistant] You literally just go, oh, okay, it's time to stop eating. Well, here's the interesting part. THC binds to those CB receptors on the POMC neurons, activating it. You're like Justin.


[User] Please transcribe this text

[Assistant] That doesn't make sense. You just told me that if the neuron fires, I feel full.


[User] Please transcribe this text

[Assistant] And now you're telling me that when THC binds to it, it causes it to fire. Doesn't that mean that you just feel more full?


[User] Please transcribe this text

[Assistant] Well, that's where things get even more interesting because the THC also goes inside of the neuron and it binds to what's called a mitochondria.


[User] Please transcribe this text

[Assistant] Mitochondria are ridiculously interesting and maybe we have to do a video on those in the future. But what ends up happening is you also get endorphin creation.


[User] Please transcribe this text

[Assistant] And when it's the combination of the endorphins and the firing of the POMC neuron, some weird strangeness occurs that we still don't fully understand, and you get hungrier.


[User] Please transcribe this text

[Assistant] Of also feeling hungry and full. So again, there's still more to figure out, but


[User] Please transcribe this text

[Assistant] I mean, that's what's going on when you're eating an entire bag of chips.


[User] Please transcribe this text

[Assistant] I also want to do a lot more, well maybe not a lot more, but some more THC and CBD and cannabis related videos because, at least here in the United States, it's becoming legalized both medicinally and recreationally.


[User] Please transcribe this text

[Assistant] Give me those ideas because I need your ideas. I need your help. But this was a really fun video to film. I hope every single one of you have a great 420. And if you are one of those who's partaking of cannabis products, now you know what's going on with your red eyes, your dry mouth and why exactly you can't stop demolishing that entire bag of chips. But I'll see you in the next video.


[User] Please transcribe this text

[Assistant] In this section, we are going to take a look at all the products of the Elastic Family, followed by the importance of analyzing your data with Elastic Stack. Finally, we will be installing and starting Elastic Search, Logstash, and Kibana in a system running with Windows operating system. Chapter 1, The Elastic Family.


[User] Please transcribe this text

[Assistant] In this video, we are going to take a look at a brief introduction about all the product offerings of Elastic and how each product fit into the stack.


[User] Please transcribe this text

[Assistant] The first and most important product of the Elastic Stack family is Elasticsearch, a distributed, full-text search server and analytic engine that is built on top of Apache Lucid. It serves as the heart of the Elastic Stack.


[User] Please transcribe this text

[Assistant] Elasticsearch was first developed by Shea Bannon and was open sourced as a distributed search engine in 2010.


[User] Please transcribe this text

[Assistant] Right from its first release, it gained popularity for its ease of use and was implemented by many companies. Later, in 2012, the Elasticsearch company was founded by the people behind Elasticsearch and Apache Lucid.


[User] Please transcribe this text

[Assistant] To better align the company with the broad solution it provided.


[User] Please transcribe this text

[Assistant] The next product in the Elastic Stack family is Lockstash.


[User] Please transcribe this text

[Assistant] An open source data collection engine with real-time pipelining capabilities. It was started by Jordan Cecil in 2009. Later in August 2013, Logstash was added to the Elastic family.


[User] Please transcribe this text

[Assistant] A few months later, a new product by the name LogStash Forwarder was introduced.


[User] Please transcribe this text

[Assistant] The main difference between logstash and logstash forwarder was that


[User] Please transcribe this text

[Assistant] Logstash forwarder was used only to ship logs from server, whereas Logstash had capabilities of converting the files to JSON document and had the ability to send to various output nodes like Kafka, File, etc.


[User] Please transcribe this text

[Assistant] The other reason being for the existence of Logstash forwarder was that Logstash is a Java based program and it was heavy to install on all the host servers. In contrary, Logstash forwarder was a lightweight product and was used to collect log files from all the servers. The next member in the product line is Kibana.


[User] Please transcribe this text

[Assistant] An open source, browser based, analytic and search dashboard for Elasticsearch. It was created by Rashid Khan in 2011.


[User] Please transcribe this text

[Assistant] Later, in Jan 2013, Rashid joined the Elasticsearch team and it became the visual front-end of the Elastic Stack.


[User] Please transcribe this text

[Assistant] Together these three products became a very powerful tool called the ELK stack or the ELF stack.


[User] Please transcribe this text

[Assistant] are a few examples among a large number of companies who were benefited by the ELK stack.


[User] Please transcribe this text

[Assistant] In May 2015, Elastic introduced the Beats family, a platform for building lightweight open source data shippers that can send data to Elasticsearch which can be analyzed later. We will be seeing in detail about the Beats platform in the upcoming slides.


[User] Please transcribe this text

[Assistant] With beads added to the product line, Elastic unveiled the Elastic Stack in Feb 2016 which consisted of


[User] Please transcribe this text

[Assistant] Elasticsearch, Lockstash, Kibana and Beats.


[User] Please transcribe this text

[Assistant] A new product by the name XPath was introduced in Feb 2016 that extends the Elastic Stack with features such as security, alerting, monitoring, reporting and graph.


[User] Please transcribe this text

[Assistant] We will look into each product of XPAC later in this video. The Beats platform.


[User] Please transcribe this text

[Assistant] A real-time network and packet analytics solution was developed by Tudor and Monika and was acquired by Elastic in May 2015. Along with this, they also unveiled the Beats platform.


[User] Please transcribe this text

[Assistant] In the same release, another beats was also introduced by the name file beats, which was the replacement of logstash forwarder.


[User] Please transcribe this text

[Assistant] Metric Beats. Metric Beats is a lightweight shipper that can be installed on servers to periodically collect metrics from the operating system and the services running on it. Previously, the product was known by the name Top Beats.


[User] Please transcribe this text

[Assistant] In Feb 2016, Windlock Beat, a lightweight shipper


[User] Please transcribe this text

[Assistant] Windows Event Logs was introduced. It runs as a Windows service and can ship event log data to Elasticsearch or Logstash. Later, in July 2016, the LibBeat framework was unveiled through which the developers can develop their own beats.


[User] Please transcribe this text

[Assistant] In January 2014, Elastic introduced their first commercial product by the name Marvel, a product which will monitor Elasticsearch deployments in real time.


[User] Please transcribe this text

[Assistant] Giving system administrators complete transparency into the state of their deployments. As part of the xpack package, it was later renamed to monitoring. Shield.


[User] Please transcribe this text

[Assistant] In Jan 2015, Elastic introduced a commercial plugin by the name Shield to enhance the security and administrative functionalities in Elasticsearch. A few features of Shield are providing role-based access control, LDAP-based authentication system support, and audit logging, alerting,


[User] Please transcribe this text

[Assistant] In May 2015, a new product providing alerting and notification capabilities for Elasticsearch was introduced, which was called by the name Watcher.


[User] Please transcribe this text

[Assistant] Watcher allowed companies that use Elasticsearch for real time search and analytics to set up alerts and notifications around the changes and trends in their data. Reporting.


[User] Please transcribe this text

[Assistant] A new future called reporting was introduced with XPAC, which was used to generate scheduled and email dashboard reporting. A new future called reporting was introduced along with the XPAC package.


[User] Please transcribe this text

[Assistant] Reporting was used to generate, schedule an email dashboard as PDF reports to any user or group to collaborate among the team.


[User] Please transcribe this text

[Assistant] In March 2016, Graph, a new extension of Elasticsearch and Kibana, was introduced which allows users to uncover, understand and explore the relationship between the data.


[User] Please transcribe this text

[Assistant] In the next video, we will be seeing how to make sense of your data using the Elastic Stack.


[User] Please transcribe this text

[Assistant] Welcome to this course on the practical aspects of deep learning.


[User] Please transcribe this text

[Assistant] Perhaps now you've learned how to implement a neural network. In this week, you learned the practical aspects of how to make your neural network work well, ranging from things like hyperparameter tuning, to how to set up your data, to how to make sure your optimization algorithm runs quickly so that you get your learning algorithm to learn in a reasonable amount of time.


[User] Please transcribe this text

[Assistant] In this first week, we'll first talk about how to settle your machine learning problem, then we'll talk about regularization, and we'll talk about some tricks for making sure your neural network implementation is correct. With that, let's get started.


[User] Please transcribe this text

[Assistant] Making good choices in how you set up your training, development and test sets can make a huge difference in helping you quickly find a good, high-performance neural network.


[User] Please transcribe this text

[Assistant] When training a neural network, you have to make a lot of decisions, such as how many layers will your neural network have? And how many hidden units do you want each layer to have?


[User] Please transcribe this text

[Assistant] What's the learning rate and what, what are the activation functions you want to use for the different layers?


[User] Please transcribe this text

[Assistant] When you're starting on a new application, it's almost impossible to correctly guess the right values for all of these, and for other hyperparametric choices on your first attempt.


[User] Please transcribe this text

[Assistant] So in practice, applying machine learning is a highly iterative process in which you often start with an idea, such as you want to build a neural network of a certain number of layers, a certain number of units, maybe on certain data sets and so on. And then you just have to code up and try it.


[User] Please transcribe this text

[Assistant] By running your code, you run an experiment, uhm, and you get back a result that tells you how well this particular network or this particular configuration works. And based on the outcome of, you might then refine your ideas and, uhm, change your choices.


[User] Please transcribe this text

[Assistant] Maybe keep iterating in order to try to find a better and a better neural network.


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[Assistant] Today, deep learning has found great success in a lot of areas ranging from natural language processing to computer vision.


[User] Please transcribe this text

[Assistant] To speech recognition, to a lot of applications on uhm, also structured data.


[User] Please transcribe this text

[Assistant] And structured data includes everything from advertisement to, uh, web search.


[User] Please transcribe this text

[Assistant] Which isn't just internet search engines, there's also, for example, shopping websites, or really any website that wants to deliver great search results when you enter terms into a search bar, uhm, to


[User] Please transcribe this text

[Assistant] computer security, uh, to logistics, uhm, such as


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[Assistant] Figuring out where to send drivers to pick up and drop off things to many more.


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[Assistant] So what I've seen is that sometimes a researcher with a lot of experience in NLP might enter


[User] Please transcribe this text

[Assistant] You know, I might try to do something in computer vision, or maybe a researcher with a lot of experience in speech recognition might


[User] Please transcribe this text

[Assistant] You know, jump in and try to do something on advertising, or someone from security might want to jump and do something on logistics. And what I've seen is that


[User] Please transcribe this text

[Assistant] Intuitions from one domain or from one application area often do not transfer to other application areas. And the best choices may depend on


[User] Please transcribe this text

[Assistant] The amount of data you have, the number of input features you have, your computer configuration and with your training on


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[Assistant] So for a lot of applications, I think it's almost impossible, even very experienced deep learning people find it almost impossible to correctly guess the best choice of hyperparameters the very first time.


[User] Please transcribe this text

[Assistant] Today, applied deep learning is a very iterative process where you just have to


[User] Please transcribe this text

[Assistant] Go around this cycle many times to hopefully find a good choice of network for your application.


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[Assistant] So one of the things that will determine how quickly you can make progress is how efficiently you can go around.


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[Assistant] Take all the data you have and carve off some portion of it to be your training set, some portion of it to be your holdout cross validation set,


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[Assistant] Uhm, and this is sometimes also called the development set. And for brevity, I'm just going to call this the


[User] Please transcribe this text

[Assistant] Devset, but all of these terms mean roughly the same thing.


[User] Please transcribe this text

[Assistant] And then you might carve out some final portion of it to be your test set.


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[Assistant] And so the workflow is that you keep on training algorithms on your training set and use your dev set or your holdup cross validation set.


[User] Please transcribe this text

[Assistant] Train test splits, if you don't have an explicit dev set or maybe, you know, a 60, 20, 20% split in terms of 60% train, 20% dev and 20% test.


[User] Please transcribe this text

[Assistant] And several years ago, this was widely considered best practice in machine learning.


[User] Please transcribe this text

[Assistant] If you have, you know, maybe a hundred examples in total, maybe a thousand examples in total, maybe up to, you know, 10,000 examples, these sorts of ratios were perfectly reasonable rules of thumb. But in the modern big data era, where, for example, you might have a million examples in total, then the trend is that your dev and test sets


[User] Please transcribe this text

[Assistant] Uhm, have been becoming a much smaller percentage of the total.


[User] Please transcribe this text

[Assistant] Because remember, the goal of the dev set, the development set, is that you're going to test different algorithms on it and see which algorithm works better. So the dev set just needs to be big enough for you to evaluate


[User] Please transcribe this text

[Assistant] Say two different algorithm choices or ten different algorithm choices and quickly decide which one is doing better. And you might not need a whole twenty percent of your data for that.


[User] Please transcribe this text

[Assistant] So for example, if you have a million training examples, you might decide that just having ten thousand


[User] Please transcribe this text

[Assistant] Examples in your dev set is more than enough to evaluate, you know, which one of two algorithms does better.


[User] Please transcribe this text

[Assistant] And in a similar vein, the main goal of your test set is giving your final classifier to give you a pretty confident estimate of how well it's doing.


[User] Please transcribe this text

[Assistant] And again, if you have a million examples, maybe you might decide that 10,000 examples is more than enough in order to evaluate a single classifier and give you a good estimate of how well it's doing. So in this example where you have


[User] Please transcribe this text

[Assistant] A million examples, if you need just 10,000 for your dev and 10,000 for your test, your ratio would be more like, uhm,


[User] Please transcribe this text

[Assistant] And I've also seen applications where if you have even more than a million examples, you might end up with, you know, 99.5% train and a 0.25% dev, 0.25% test, or maybe a 0.4% dev, 0.1% test. So just to recap, when setting up your machine learning problem, more often,


[User] Please transcribe this text

[Assistant] Set it up into a trained dev and test sets, and if you have a relatively small data set, these traditional ratios might be okay, but if you have a much larger data set, it's also fine to set your dev and test sets to be much smaller than, you know, 20% or even 10% of your data.


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[Assistant] We'll give more specific guidelines on the sizes of dev and test sets later in this specialisation. One other trend we're seeing in the era of


[User] Please transcribe this text

[Assistant] Modern deep learning is that more and more people train on mismatched train and test distributions. Let's say you're building an app.


[User] Please transcribe this text

[Assistant] Might comprise cat pictures from users using our app. So mirror training set has a lot of pictures crawled off the internet.


[User] Please transcribe this text

[Assistant] But the dev and test sets are pictures uploaded by users. Turns out a lot of webpages have very high resolution, very professional, very nicely framed pictures of cats, but maybe your users are uploading, you know, blurrier, lower res images just taken with a cell phone camera in a more casual condition.


[User] Please transcribe this text

[Assistant] And so these two distributions of data may be different. The rule of thumb I'd encourage you to follow in this case is to make sure that the dev and test sets come from the same distribution.


[User] Please transcribe this text

[Assistant] We'll say more about this particular guideline as well, but because you will be using the DevSet to evaluate a lot of different models and try really hard to improve performance on the DevSet,


[User] Please transcribe this text

[Assistant] It's nice if your dev set comes from the same distribution as your test set.


[User] Please transcribe this text

[Assistant] But because deep learning algorithms have such a huge hunger for training data.


[User] Please transcribe this text

[Assistant] One trend I'm seeing is that you might use all sorts of creative tactics such as crawling webpages in order to acquire a much bigger training set than you would otherwise have. Even if part of the cost of that is then that your training set data might not come from the same distribution as your dev and test set. But you find that so long as you follow this rule of thumb,


[User] Please transcribe this text

[Assistant] It might be okay to not have a test set. Remember the goal of the test set is to give you an unbiased estimate of the performance of your final network, of the network that you selected. But if you don't need that unbiased estimate, then it might be okay to not have a test set.


[User] Please transcribe this text

[Assistant] So what you do if you have only a dev set but not a test set is you train on the training set.


[User] Please transcribe this text

[Assistant] Uhm, and then you try different model architectures, evaluate them on the dev set.


[User] Please transcribe this text

[Assistant] And then use that to iterate and try to get to a good model. Because you've fit your data to the dev set, this no longer gives you an unbiased estimate of performance, but if you don't need one, that might be perfectly fine.


[User] Please transcribe this text

[Assistant] In the machine learning world, when you have just a train and a dev set, but no separate test set, most people will call this the training set, and they will call the dev set.


[User] Please transcribe this text

[Assistant] The test set. But what they actually end up doing is using the test set as a holdout cross validation set.


[User] Please transcribe this text

[Assistant] Which maybe isn't completely a great use of terminology because they're then overfitting to the test set.


[User] Please transcribe this text

[Assistant] So when a team tells you that they have only a train and a test set, you know, I would just be cautious and think, do they really have a train?


[User] Please transcribe this text

[Assistant] Uhm, culturally it might be difficult to change some of these teams' terminology and get them to call it a trained dev set rather than a trained test set.


[User] Please transcribe this text

[Assistant] Even though I think calling it a trade and development sets would be more correct terminology.


[User] Please transcribe this text

[Assistant] And this is actually okay practice if you don't need a completely unbiased estimate of the performance of your algorithm.


[User] Please transcribe this text

[Assistant] So having set up a train, dev, and test set will allow you to iterate more quickly. It will also allow you to more efficiently measure the bias and variance of your algorithm so that you can more efficiently select ways to improve your algorithm. Let's start to talk about that in the next video.


[User] Please transcribe this text

[Assistant] This video is sponsored by Noom. Find the link in the description below.


[User] Please transcribe this text

[Assistant] We all know that we need to be drinking water, but why do we need to be drinking water? What makes it so important?


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[Assistant] What does the body even do with it? And what happens to the body when you don't have enough of it? Are these simple questions? Maybe.


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[Assistant] But they're important questions that everyone should have the answers to. And in today's video, we're going to use the cadavers to take a look at what happens to water once it enters into the body through the digestive system.


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[Assistant] We'll see how the brain even understands that you need more or less water, how the kidneys respond to the brain's instructions, and we'll even see what happens to cells when they have too much, too little, or just the right amount of water. It's gonna be a fun one. Let's do this.


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[Assistant] First occurs when there's a need for an organism such as you to find and consume water. As you probably already know, without water,


[User] Please transcribe this text

[Assistant] You die within a few days, depending on the exact circumstance. I mean, if you're in the Sahara Desert with no protection from the elements, you're likely to die a whole lot quicker than you would if you were in other environments. But all in all, you tend to die within a few days. In contrast, humans can survive without food for weeks to months at a time, again, depending on the exact circumstances.


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[Assistant] You can find it inside of the cells or what we call the intracellular space or you can find it outside of the cells in what we call the extracellular space.


[User] Please transcribe this text

[Assistant] Roughly two thirds of your total water volume is going to be located in the intracellular space, meaning most of your water is going to be in the cells of your brain, your heart, your lungs, your kidneys, so on and so forth. The remaining one third will be found outside of your cells, so think like you're connective tissues.


[User] Please transcribe this text

[Assistant] Now this means, depending on the type of dehydration you're experiencing, you could actually experience different types of thirst. An intracellular thirst or an extracellular thirst.


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[Assistant] Let's say that if your cells become dehydrated, often times simply drinking water can be enough to replenish them. However, if you lose blood because blood contains water, let's say you get a cut on your arm, that could mean that you'd need water and salt


[User] Please transcribe this text

[Assistant] To help regenerate the blood at its correct concentrations. This could mean you might need a meal to go with your water to fully quench your thirst, although I'm not entirely sure that's the best way to describe it. You might be wondering how much water you should be drinking per day. That's actually a little more difficult to answer than you may think, and that's because it depends on a variety of factors.


[User] Please transcribe this text

[Assistant] What altitude do you live at, believe it or not? Uhm, how old are you? Are you menstruating? Do you consume alcohol? How physically active are you? The list goes on and on. And while these are all important questions to ask,


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[Assistant] And then start to dial in the exact amount you need for you as a person.


[User] Please transcribe this text

[Assistant] Based on your individual circumstances, but that's true of health and wellness in general. If you wanna make an overall improvement in your health,


[User] Please transcribe this text

[Assistant] Well first you need to educate yourself as to why it's actually important and a good idea to make that improvement and then you need to build a healthy habit off of that and then you can even start to expand later on down the road.


[User] Please transcribe this text

[Assistant] It's, to me, undeniably the best way to go. That's why I love the sponsor of today's video, Noom.


[User] Please transcribe this text

[Assistant] I've been using Noom for a little over a month now, and I am head over heels for it, and the reason is super simple.


[User] Please transcribe this text

[Assistant] It's not your run-of-the-mill fitness tracker. It's so much more than that. I mean, yes, it does do what those other fitness trackers you've seen do. It counts your calories, it tracks your steps and your body weight, but it does something else on top of that that is so vitally important and I see no one else doing it.


[User] Please transcribe this text

[Assistant] And it does that through articles and lessons, which are super short by the way. You can easily get through your daily articles and lessons in less than 10 minutes. On top of that, with Noom, you get your own goal specialist that's trained in psychology, nutrition, and fitness to help guide you through this educational journey that you're going on. And the crazy part is they're a real person.


[User] Please transcribe this text

[Assistant] I tested this because I didn't believe it at first. I was like, you know, maybe they just say it's a real person, but it's really an artificial intelligence. So what I did is I got in contact with my goal specialist and I was like, I'm Justin from the Institute of Human Anatomy and I kind of told them how I was going to be doing this very sponsored post.


[User] Please transcribe this text

[Assistant] And I asked them, do you have anything you wanted to share with me? Maybe like your experiences working with people as a goal specialist or feel free, whatever. And I got this huge response just being like, this is so cool. Your content is amazing. And then listed off all these things, these success stories, tips, tricks. And I'm just sitting there like,


[User] Please transcribe this text

[Assistant] This is amazing. They genuinely, genuinely want to help you. Pneuma Special because they're doing the only thing that I've personally found to work long term. Build better habits.


[User] Please transcribe this text

[Assistant] Better sleep habits, fitness habits, eating habits, right? It's not about fad dieting or fad exercises.


[User] Please transcribe this text

[Assistant] And the thing is, they do it all without shaming you. I mean, if you miss a way in, or maybe you don't read all your articles or blogs, instead of shaming you, your goal specialist will just contact you and say, hey,


[User] Please transcribe this text

[Assistant] What's going on? Is there anything I can do to help? Is there anything you need? I mean, it's, it's really humanizing and just says, you know what? Look, we all make mistakes. We all have slip ups.


[User] Please transcribe this text

[Assistant] What can I do to actually help? If you're interested, click the link in the description below or you just visit Noom.com slash Human Anatomy and take your free Noom evaluation today. It's quick, it's easy, and it's a first step in building better overall health habits.


[User] Please transcribe this text

[Assistant] Let's go ahead and jump over to a cadaverous section of the brain and see just how the body monitors hydration levels. You're looking at a midsagittal cross section of the human head, meaning we've cut it right down the middle. So to quickly orient you so you know what you're looking at, this is the anterior direction, this is going to be the nose, this is that nasal septum.


[User] Please transcribe this text

[Assistant] And then we're heading in the posterior direction. And this really cool looking structure here is called the cerebellum. And then right in front of it is the brain stem. But our focus today is going to be in this little region right here. So specifically what I'm tracing with my probe is a region called the hypothalamus.


[User] Please transcribe this text

[Assistant] And then just below the hypothalamus is a really fascinating gland called the pituitary gland. Now both of these structures together form what you can think of as like a bridge


[User] Please transcribe this text

[Assistant] Between the neurological or nervous system, like the brain that I'm touching with the probe here.


[User] Please transcribe this text

[Assistant] And your glandular hormonal endocrine system, like the adrenal glands for instance.


[User] Please transcribe this text

[Assistant] The job is to try and bring the body back to homeostasis or at times actually bring it away from homeostasis. It really depends on the circumstance. But a real easy thing to wrap your mind around would be like body temperature.


[User] Please transcribe this text

[Assistant] Which is about 98.6 degrees Fahrenheit, or I think it's 36 to 37 degrees Celsius. I forget which one it is. But the body, or the hypothalamus, will deviate from that based on demand. So maybe say if you're hot.


[User] Please transcribe this text

[Assistant] Then it will recognize that as you're going above that temperature and it will start to initiate sweating to help bring you to cool down. Well, what goes with that homeostasis is going to be water regulation. Now, the term we're actually gonna use here is osmoregulation.


[User] Please transcribe this text

[Assistant] Now to understand what that means, we first need to discuss something called osmosis. Simply put, osmosis is the diffusion or movement of water from one area to another based around electrolyte concentration. Now you've probably heard of electrolytes before.


[User] Please transcribe this text

[Assistant] Probably in sports drinks, but these are gonna be things like sodium, chloride, potassium to name a few and they are all over the body.


[User] Please transcribe this text

[Assistant] They are extremely important for cellular function and just performance and in this instance they are essential for monitoring and regulating hydration levels. So inside of the hypothalamus, which again is going to be this region right here,


[User] Please transcribe this text

[Assistant] are actually stretch-sensitive neurons. And what happens is they are capable of actually determining how much water and how much electrolytes are overall in the body based around how many, how much water and electrolytes are inside of them.


[User] Please transcribe this text

[Assistant] So think about it like this. If there is not enough water, those osmoreceptors will actually shrink. And that will send a signal down to this area right here called the posterior pituitary.


[User] Please transcribe this text

[Assistant] And tell it to secrete a hormone called anti-diuretic hormone.


[User] Please transcribe this text

[Assistant] You've probably heard of diuretics before. A diuretic is something that causes you to urinate. So an anti-diuretic hormone would cause you to


[User] Please transcribe this text

[Assistant] So, if you don't have enough water, it makes sense to not urinate. You don't want to lose anymore, so what will happen is that we'll go down to the kidneys and say, hey, let's not urinate.


[User] Please transcribe this text

[Assistant] But, let's say you have too much water. That would then cause those osmoreceptors to start to swell. Right? They start to bloat. And that itself would also send a signal down to this posterior pituitary and say,


[User] Please transcribe this text

[Assistant] Because if you make less of it, then obviously you're going to urinate more and you can start to lose water.


[User] Please transcribe this text

[Assistant] One other example of neurons that are osmoregulatory are what are called baroreceptors. These are going to be located inside of blood vessels, specifically arteries like the aorta which is going to be coming out from the heart, and they are monitoring blood pressure.


[User] Please transcribe this text

[Assistant] So if you picture this, right, there's water in your blood, so if you have too much water in the bloodstream, that's going to elevate blood pressure. And these baroreceptors are also stretch sensitive, and they are going to detect that, and they are then going to initiate a series of events that is going to be osmoregulated.


[User] Please transcribe this text

[Assistant] At the same time, let's say maybe you get cut. I don't know, maybe like in the knee or something along those lines and you're bleeding.


[User] Please transcribe this text

[Assistant] Well, you are now losing water, and so what's gonna happen is you're gonna have a slight decrease in blood pressure.


[User] Please transcribe this text

[Assistant] You're looking at a kidney that's been cut in the frontal or coronal plane, which means it's been divided into front and back portions. Now I'm going to give you a quick tour of the kidney because there's a lot to look at here.


[User] Please transcribe this text

[Assistant] So this is called the renal artery, and what it does is it brings unfiltered blood to the kidney, because that's what the kidney does, it filters your blood. And this renal artery is going to start splitting, and it splits many different times until eventually these tiny little blood vessels get to this outer rim, which you can see this distance here.


[User] Please transcribe this text

[Assistant] Is called the renal cortex. This is where all the really interesting stuff happens inside of the kidney. And that's because there are these tiny little filtration units located in here that we can't see, which are called nephrons.


[User] Please transcribe this text

[Assistant] They're going to filter the blood and then that newly filtered blood will basically take the same return trip.


[User] Please transcribe this text

[Assistant] Except instead of going in the arterial system, it's gonna go in the venous system, which is, if I can grab it, that's this one behind it right there. Now.


[User] Please transcribe this text

[Assistant] When it filters, uh, the waste out, it's going to drop it into this darkened, uh, inner core of the kidney. This region here is called the renal medulla.


[User] Please transcribe this text

[Assistant] And the renal medulla is made up of these individual renal pyramids. That's what all these darkened portions are.


[User] Please transcribe this text

[Assistant] The renal pyramids are basically just made up of all these little tubes. What will happen is the waste product will drop into the collecting tubes and then go down into the urinary tract. And that's what you're seeing here, which will eventually go down to like the ureter and then down.


[User] Please transcribe this text

[Assistant] Now in terms of osmoregulation, that is going to occur here in that renal medulla. So what you have to understand is that the tissue of the kidney itself is going to be naturally salty. It's going to have a bunch of sodium and chloride inside of there and that is going to attract water. So,


[User] Please transcribe this text

[Assistant] Just think about it like this. If you have too much water, what will happen is the kidney will actually put the salt into the collecting tubes.


[User] Please transcribe this text

[Assistant] And that will attract water into those renal pyramids, and then you will urinate out the excess water.


[User] Please transcribe this text

[Assistant] Now, if you are dehydrated, what will happen is that salt will move into the, more so into the kidney tissue and the water will come with it.


[User] Please transcribe this text

[Assistant] So the water will go into the tissue of the kidney which will then find other accessory blood vessels and be returned


[User] Please transcribe this text

[Assistant] Well, it depends on, again, the type of dehydration that's happening to you. Remember, blood loss actually is a form of dehydration, and with enough blood loss, you have some pretty severe problems on your hand. But it also might be important to discuss the different ways you can lose water to begin with, because you don't just lose water through urination.


[User] Please transcribe this text

[Assistant] You lose water through breathing and speaking, through crying, through sweating.


[User] Please transcribe this text

[Assistant] And it's not just water you're losing in these instances either. You're also going to be losing electrolytes. Remember, those salts are what attract water to it. And so in order to get rid of the water, you're also going to be getting rid of some of those electrolytes, which is going to definitely


[User] Please transcribe this text

[Assistant] It depends on the form of dehydration. Are you just dehydrated from water? Are you just dehydrated from electrolytes? Or is it both? And in what amounts? But in the end, it's going to affect the performance of the cells, the individual cells themselves.


[User] Please transcribe this text

[Assistant] How much is it going to affect their performance? Again, it also depends, right? I mean, just think about the common symptoms you see with dehydration. Headache, dry mouth, lips, eyes. You can get a darker color of urine. I'm sure you've all seen that, where that's because there's a high, when there's less water content in the urine,


[User] Please transcribe this text

[Assistant] And there's a higher density of other things, it just tends to look darker. But you're also gonna get confusion. You'll get fatigue.


[User] Please transcribe this text

[Assistant] And the worse the dehydration gets, the worse these symptoms will get. To the point where it's not just confusion in the late stages of dehydration.


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[Assistant] Borderline delirium to the point where you're psychotic. So what's happening to the body? Essentially you're just dying.


[User] Please transcribe this text

[Assistant] And you're dying very slowly in the initial stages and then you're dying very quickly in those later stages. Thanks again to the sponsor of today's video Noon. Be sure to click the link in the description below and take their quick and easy free evaluation and you can start building better health habits today. As always, be sure to like, comment, subscribe if you haven't already and


[User] Please transcribe this text

[Assistant] Good luck hydrating out there, folks. Be sure to consume electrolytes on top of that water to optimize these hydration levels, and I will see you in the next video.


[User] Please transcribe this text

[Assistant] Almost every single one of us will experience some form of back pain at some point in our lives. Now obviously that can vary in severity, how bad it hurts, how long it lasts, and of course what the actual cause of that back pain is. So in today's video, we're going to be taking a look at the back.


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[Assistant] The spinal bones, nerves, discs and surrounding muscles, essentially looking at all these places where potential problems can occur so that you can better understand why you might be experiencing that back pain. So let's jump right into it.


[User] Please transcribe this text

[Assistant] So as I mentioned, there are multiple spots or places throughout the back or spine where we could have potential problems and therefore pain. So it probably makes sense for us to go over some of the basic features or anatomy of this spine, also known as the vertebral column. Those can be used interchangeably.


[User] Please transcribe this text

[Assistant] And we can see that the spine is made up of multiple or individual bones that we call vertebrae.


[User] Please transcribe this text

[Assistant] And it's also broken down into five regions. And just so you can see this a little bit more closely, let's break this down on the tray. As you can see, we've got this wonderfully organized tray of vertebral bones or vertebrae.


[User] Please transcribe this text

[Assistant] And let's take a look at some of these structural features so that we can apply them to some of the origins of back pain. Now remember we said there's five individual regions that make up the spine.


[User] Please transcribe this text

[Assistant] This top row here is our first region referred to as the cervical region. Now the cervical region or cervical translates to neck.


[User] Please transcribe this text

[Assistant] And there are seven individual cervical bones or cervical vertebrae that make up this region. Here's cervical vertebra number one, cervical vertebra number two. You can see how annoying that would be. So instead we choose to abbreviate C1, C2, all the way down to C7.


[User] Please transcribe this text

[Assistant] The next two rows represent the next region, which is the thoracic region. You can see the bones are also getting larger. Thoracic region just refers to chest as this is associated with rib and other chest anatomy. And there are 12 individual thoracic vertebrae. Here's T1 all the way down to T12 here.


[User] Please transcribe this text

[Assistant] The next region is the lumbar region represented by this row. Lumbar translates to loin.


[User] Please transcribe this text

[Assistant] Now, we're not really referring to loin as in the context that you're probably thinking of. We're not referring to like a stirring or a rustling in your loins or nether regions here. In anatomy, loin refers to this region between the lower rib and the pelvis here, essentially this area where the lumbar vertebrae occupy.


[User] Please transcribe this text

[Assistant] Now I want to just pause for a second here, because these top three regions, the cervical, thoracic, and lumbar, are known as the mobile or movable portion of the vertebral column. Meaning, you can get movement between the individual bones, like between L1 and L2, or C4 and C5.


[User] Please transcribe this text

[Assistant] And that is great and all because we get mobility, but whenever we get mobility, there is an increased risk for injury, damage, pain. And that's gonna be a little bit different when we compare it to the next two regions or those bottom two regions. The next one or the fourth one being here. Let me get this into focus.


[User] Please transcribe this text

[Assistant] In between my fingers, called the sacral region. The sacral region started as five individual sacral bones. So you had an S1, 2, 3, 4, and 5. But around age 14 to 16, these bones start to fuse together. And by the time you're about 30, the final fusion takes place and it is one solid bone that we refer to as the sacrum. The fifth region does something similar. The coccygeal region that you can see here.


[User] Please transcribe this text

[Assistant] was individual little bones that you can see here that fused together. Most of us had four individual coxagial bones, but some of us will have three, some of us will have five, but again, the majority have four.


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[Assistant] One of those four fused together, we call it the coccyx or many of us just refer to it as the tailbone. So now that we've gone over some of these structural features, we can really start to hone into some of these origins of back pain. And one of the things I like people to think about is what are some of the functions or demands that we place upon the back or the spine?


[User] Please transcribe this text

[Assistant] One of those is to protect the spinal cord. And if you look at these vertebrae again, you can see they all have these holes, and this hole is referred to as the vertebral meramen. Now if we stack those vertebrae on top of each other again, it would create a canal called the vertebral canal, and that's where the spinal cord will run through.


[User] Please transcribe this text

[Assistant] Another demand we place upon the spine is to transmit forces or weight through the central skeleton.


[User] Please transcribe this text

[Assistant] This large region that you see on the majority of the vertebrae here is referred to as the vertebral body, and that's going to transmit forces and weight. Now this little hole that's in these vertebral bodies isn't actually not supposed to be there. It's more so if we just needed to string it up for an anatomical model, but just wanted to mention that.


[User] Please transcribe this text

[Assistant] But the vertebral body is also associated with the intervertebral disc, which we simply often refer to as the disc.


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[Assistant] You can see body, body, disc in between, has a nice little shock absorbing pad, but we also know that the disc can cause some potential problems. Another thing we ask of the spine is to move in all three planes of space. It has cool lever arms that you see on the vertebrae here.


[User] Please transcribe this text

[Assistant] And these sideways ones, or ones going off to the side, are called transverse processes. The ones coming off the back are called the spinous processes. But these are attachment sites for some of these big spinal muscles so they can pull on these bones to move side to side, front to back, and even rotate. So, spine. Protect the spinal cord.


[User] Please transcribe this text

[Assistant] Transmit all this weight. And then, move in all three planes of space. And by the way, try not to ever get hurt.


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[Assistant] I have to joke around with my students and say that it's no wonder people have back pain because of all the demands that we place upon the spine.


[User] Please transcribe this text

[Assistant] So let's start going into some of these specific causes, like for example, the intervertebral disc that we already mentioned. And if we fast forward into that conversation, one of the things that can help decompress or take some of the pressure off your disc is sleep.


[User] Please transcribe this text

[Assistant] Which is a nice little segue for me to say thank you to the sponsor of today's video, Helix Sleep.


[User] Please transcribe this text

[Assistant] Helix Sleep provides premium mattresses and bedding that are customized to fit your needs, and they are shipped to the comfort of your own home. And Helix Sleep knows that everyone's sleep needs are a little bit different, so they created this sleep quiz where you can get on there and customize the mattress based on body


[User] Please transcribe this text

[Assistant] As well as sleep preferences. When I got on there, I mentioned that I tend to be a back sleeper. I don't like too soft of mattresses but also not too hard, so I'm right in the middle and I wanted something that was a medium firmness. If you sleep with another person, they can also get involved in the sleep quiz so that you can customize a mattress that fits both of your needs.


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[Assistant] And I ended up getting something called the Helix Midnight Lux.


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[Assistant] Let me tell you, I've had this thing for about a week and a half, and I love it. For multiple reasons. One, it's super fun to just open this thing up and watch it expand.


[User] Please transcribe this text

[Assistant] Two, I'm sleeping more comfortably, more soundly, it just already feels better. And three,


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[Assistant] I'm not being a hypocrite, creating a video on spinal health, and then sleeping on a crappy mattress. I'm sleeping on a good mattress. No, a great mattress. And one other thing I think is important to mention is that getting a mattress can be a big purchase, so you want to make sure you get the right one.


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[Assistant] If you've ever been shopping for mattresses in person, you've probably gone from one mattress to another and then you're lying on this thing thinking, well this feels good.


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[Assistant] But will it feel good for eight hours? Night after night after night?


[User] Please transcribe this text

[Assistant] Well the great thing about Helix Sleep is they offer the 100 nights sleep trial, which means you can try one of their mattresses for 100 nights, and if you don't like it, they'll come get it for you and give you a full refund.


[User] Please transcribe this text

[Assistant] They also offer a 10 year warranty and flexible payment options. So if you're interested in getting one of these amazing mattresses, go to helixsleep.com slash IOHA for up to $200 off your purchase of a Helix Sleep Mattress Plus.


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[Assistant] Keep in mind, Jeffrey's all up in arms about it because he's a real skeleton, he's a fake skeleton, but Jeffrey doesn't have any examples of discs. And Cornelius doesn't have any real discs, but this cadaver dissection that we have does. So we're going to take a look at the sagittal cut through this cadaver.


[User] Please transcribe this text

[Assistant] And you can see the spine coming up like so and these amazing intervertebral discs. You can see the difference between the bone here and the disc in between the bones.


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[Assistant] Now, what's really cool about this cadaver dissection is if you look closely,


[User] Please transcribe this text

[Assistant] You can look at these discs and see that they actually are narrowing on the back and the front, or posteriorly and anteriorly, starting to degenerate a little bit. And if we move up, that degeneration got even worse in the thoracic region. You can barely make out the discs and even the vertebral bodies, the bones.


[User] Please transcribe this text

[Assistant] As the bones on that cadaver, the body in the front started to narrow and create more of a wedge shape rather than this nice boxy vertebral body that we should have. And so when you're thinking about this degenerative process, yes, you get pain from the bones degenerating as well as the disc.


[User] Please transcribe this text

[Assistant] That's one of the ways that you can have disc problems is just aging and degeneration. But many of you have met someone or maybe you've experienced just damaging or injuring your disc maybe from lifting something really heavy or just really bad luck and twisting funny.


[User] Please transcribe this text

[Assistant] And in that case, we tend to refer to it as a herniated or bulging disc. And luckily, Cornelius has taken one for the team to show us kind of this example of what a bulging disc would look like. And again, that bulge or that damage to the disc in and of itself would cause pain.


[User] Please transcribe this text

[Assistant] Now you might be thinking, well why does he keep saying that in and of itself could cause pain? Obviously, herniating a disc would cause pain. Bones degenerating would cause pain. Well, that's one category of pain.


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[Assistant] And the other category of pain is essentially potential irritation or damage to structures that are close by. And some of these close by structures are your spinal nerves, represented in yellow here.


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[Assistant] And you can see those spinal nerves are coming out of this hole called the intervertebral foramen or foramina for plural. And this is the pathway in which the nerves communicate with the spinal cord inside.


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[Assistant] But you can see, if anything were to cause this hole to get narrowed, that intervertebral foramen, maybe the actual vertebral bodies degenerated and got thinner, or the disc.


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[Assistant] And cause a problem for that nerve trying to come out there. Now another way to narrow that pathway or to compress the nerve is to have a herniated or bulging disc. Especially if it goes posterior laterally towards the nerve. And again that's just another way of narrowing that pathway and compressing irritating or damaging that nerve.


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[Assistant] So this could potentially happen throughout the spine where we have discs and associated nerves. Again, we said from C2 down to the actual sacrum.


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[Assistant] And when we talk about this nerve pain, that's another type of pain on top of that mechanical pain that I kept harping over earlier in the video.


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[Assistant] But that nerve pain can be this sharp shooting pain in the pathway of the nerve.


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[Assistant] You can also get things like numbness and tingling in the skin and even potential weakness as some of these nerves will control skeletal muscle tissue. And one last thing I want to mention about discs and nerves before we talk about other potential causes of back pain.


[User] Please transcribe this text

[Assistant] And that is common place where you often hear about herniated discs, and that is the lower lumbar region. And when you talk about herniating lumbar discs, that often comes with a discussion around the sciatic nerve and sciatica. So let me show you the sciatic nerve.


[User] Please transcribe this text

[Assistant] Here is the right gluteus maximus reflected, so right backside of the body, right side of the hip, and then the hamstrings coming down this direction, and you can see this large nerve, largest nerve in the human body, called the sciatic nerve. It would continue on the backside of the thigh, down the back of the lower leg, and into the foot.


[User] Please transcribe this text

[Assistant] But what does the sciatic nerve have to do with herniating lower lumbar discs? Well, the sciatic nerve gets contributions from five different spinal nerves or five different spinal levels. If you look at Cornelius here, we've got L4, L5 there, then S1,


[User] Please transcribe this text

[Assistant] S2 and even this S3 nerve, if I can rotate it for you, would contribute to the sciatic nerve.


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[Assistant] So those five different spinal levels. And you're thinking, well, that would make sense then. If I herniated disc at L4-L5, that would affect those L4-L5 contributions to the sciatic nerve.


[User] Please transcribe this text

[Assistant] But don't think S1 and S2 and S3 are off the hook, because even though you're looking at Cornelius' herniated disc here, it's more of a lateral, kind of just going straight out protrusion, you can have herniations that are more directly backwards, posterior, and more medial,


[User] Please transcribe this text

[Assistant] Which would go into the spinal canal and these nerves before they exit down here are running through that spinal canal and could get pushed on by herniated disc that's going posteriorly. So you could have those symptoms of sciatica.


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[Assistant] Shooting pain down the back of the leg and into the foot, numbness tingling, and even potentially muscle weakness. Another potential cause of back pain is a form of arthritis called osteoarthritis.


[User] Please transcribe this text

[Assistant] And often when we think of osteoarthritis, we point to things like knee arthritis, hip arthritis, maybe even arthritis in the joints of the fingers. And this type of arthritis is when the cartilage covering the ends of the bones that are forming the joints


[User] Please transcribe this text

[Assistant] Starts to break down or degenerate. That cartilage breaks down, degenerates, and eventually can cause pain within the joint.


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[Assistant] And on the backside or the posterior aspect of the spine of the vertebral column, there's a whole bunch of these tiny small little joints going the entire length that have cartilage within.


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[Assistant] And if we use Jeffrey here, we can see one of those joints here, here, here, here, and all the way up on both sides. You'll see these joints at every spinal level, essentially between C1, C2, C3, from cervical down to lumbar, and they're referred to as facet joints. I think a lot of people will sometimes even pronounce them as facet joints, but most


[User] Please transcribe this text

[Assistant] I could say facet because it sounds more sophisticated. But let's show you this in a little bit more detail.


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[Assistant] Here's two lumbar vertebrae. Let's zoom into one of them so I can really drive this point home. But here would be an area covered with cartilage on this lumbar vertebra. Here would be another area and these would form the set joints.


[User] Please transcribe this text

[Assistant] Then we'd have cartilage covering here, as well as cartilage covering here, again forming some facet joints with the vertebra below. And if I bring the other vertebra together, you can see how they would come together.


[User] Please transcribe this text

[Assistant] And where my probe is, if that cartilage were to break down between those two bones in that facet joint, that's a form of arthritis and can eventually lead


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[Assistant] Another type of back pain. And again, that could be anywhere within the length of the spine. And it does tend to localize close to the area where the arthritis is. And you might be thinking, well, what causes this? Well, one thing is injury puts somebody at higher risk. People, for example, who get whiplash are more likely to develop that type of arthritis in the neck.


[User] Please transcribe this text

[Assistant] Uhm, wear and tear can cause it. Uhm, you can even talk about improper mechanics, maybe an abnormal curve in the spine.


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[Assistant] Might put more pressure or more wear and tear on some facets, other or facet joints more than others. And of course, aging is a risk factor.


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[Assistant] And finally, the last type of back pain we're going to talk about is good old fashioned muscular back pain. Now this is the type of back pain that people will often say things like, I threw out my back. Now some people will say they threw out their back when they re-injured like an old disc injury, but most are referring to this muscular type of back pain.


[User] Please transcribe this text

[Assistant] Now to be clear about this muscular type of back pain, if you look at this cadaver dissection,


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[Assistant] You might not recognize a lot of these muscles here. Some of the most recognizable muscles that we're used to seeing like at the gym or in pictures are like the trapezius or traps here, the latissimus dorsi or lats would be down here, even the rhomboids between the two scapulae, which you can see right there. And the reason that we remove those is because those muscles, even though they are on the back,


[User] Please transcribe this text

[Assistant] Are actually muscles that move the limbs, like the shoulder joint and even the scapulae here.


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[Assistant] And our true back muscles that actually move our spine are underneath. And that's what you're looking at here. The way to remove all those more superficial back muscles.


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[Assistant] So for example, here's a group of muscles called the erector spinae muscles. They're made of three, like the iliocostalis here, the longissimus here, and the spinalis. FYI, people have a nice little mnemonic to remember those muscles. It's called I Love School.


[User] Please transcribe this text

[Assistant] Other people like, no, no, no, no, I remember it better this way. I'm just the messenger here, just FYI. They say something like, I love sex.


[User] Please transcribe this text

[Assistant] They are the erectors after all. But then there's also some other muscles that are deep to the erectors on this side, like the multifidus.


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[Assistant] And even the semi-spinalis. Now I know each kind of just did some name dropping there, but those are some of the names or some of the major muscles that could be involved in some muscular back pain. Not all of them, but some potential major contributors. And so what's happening with this type of pain is that the muscles can be strained.


[User] Please transcribe this text

[Assistant] Your deep back muscles can be strained or pulled just like you can strain or pull a hamstring.


[User] Please transcribe this text

[Assistant] And when you strain a muscle, the muscles can get tears in those muscle fibers and that can obviously vary in severity. Another thing that can happen is that the muscles can have a spasm or tension within them and people will often describe that as they feel tight and they don't want to move because that will aggravate the pain.


[User] Please transcribe this text

[Assistant] Now, an interesting question might be, can you have more than one thing going on at the same time? And the answer is yes. I've had patients come in where they've re-aggravated a disc injury or had a bulging disc, and the muscles surrounding that area also spasmed and created tension and pain on top of the disc injury. I even had one patient recently who had a bulging disc, pushing on the nerve, arthritis in the same area, and


[User] Please transcribe this text

[Assistant] The muscles were spasming. So as we're wrapping all of this up, I think it is important to note that we obviously didn't cover every single type of back pain. We mostly focused on these mechanical forms of back pain that are actually coming from the spine or some of those closely related structures like the nerves and the surrounding musculature that affects the spine.


[User] Please transcribe this text

[Assistant] Yes, we could talk about traumatic fractures of the spine, even pathological fractures from certain disease states. Cancer can even sometimes spread into the spine and create a problem. There's also viscerogenic back pain. Viscero just means internal organs. We know the kidneys refer to the back. The pancreas is another example when it's inflamed, can refer to the back and cause that back pain.


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[Assistant] But when we're talking about the type of back pain that we referenced in this video or focused on in this video, you might think, well, how do we deal with this? Well, luckily for a lot of people, these types of back pain can get better or improve with time and supportive therapies. And when we're talking about supportive therapies, we're talking about things like


[User] Please transcribe this text

[Assistant] Possibly applying heat, massage, exercise, and even physical therapy. You can even use things like topical lidocaine patches, NSAIDs which are things like ibuprofen to help manage the pain. You can even step up to muscle relaxers if necessary if we've got some muscles that are spasming like crazy.


[User] Please transcribe this text

[Assistant] And if those more conservative approaches fail and people are still having issues, you can step up even further to things like injections. Anti-inflammatory injections can be placed around the area where there's an inflamed nerve or even an inflamed facet joint.


[User] Please transcribe this text

[Assistant] There's this really cool procedure called a radiofrequency ablation that can also be used for those inflamed facet joints where they inject this needle with radiofrequency and what it does is essentially heats the nerve or creates this heat lesion.


[User] Please transcribe this text

[Assistant] Not the nerves that we saw on Cornelius, those big nerves, but a tiny tiny little nerve around the facet joint that's sending pain. And that heat lesion can stop that little tiny nerve from sending pain and give somebody relief in that case. There's obviously also surgical procedures that we could step up to.


[User] Please transcribe this text

[Assistant] Doing some things with a ruptured or bulging disc to help with those cases that


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[Assistant] Are improving again with some of those conservative therapies. So, protect that spine of yours.


[User] Please transcribe this text

[Assistant] Obviously there's so much more information that we could go into, but the intent of this video was to get us started on understanding the main forms of this mechanical type of back pain. But we would love to hear from you guys. So if you want us to go into any more detail of any of these different forms of back pain, throw those in the comments below so that we can make some future videos.


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[Assistant] If you're interested in getting a new mattress, go ahead and check out the link below with Helix Sleep.


[User] Please transcribe this text

[Assistant] If you like what you saw, or you feel the need, like, subscribe, turn on your notifications, and while you're waiting for the next video, go ahead, lift with proper mechanics, mobilize the spine properly, and strengthen those surrounding muscles. And we'll see you next time.


[User] Please transcribe this text

[Assistant] The next time she went to see her physician, he performed an electrocardiogram, and this showed that she'd had a large heart attack or a myocardial infarction in medical parlance. After this heart attack, Donna was never quite the same.


[User] Please transcribe this text

[Assistant] Got to the point where she couldn't keep up with her grandkids and it was even too much work to go out to the end of the driveway to pick up the mail. One day her granddaughter came by to walk the dog and she found her grandmother dead in the chair.


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[Assistant] The last thing that I should tell you is that Donna was not just an ordinary patient. Donna was my mother.


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[Assistant] This leaves the patient with a deficit in the amount of heart muscle that they have.


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[Assistant] I think there's a better way, and this better way involves the use of stem cells as medicines. So what exactly are stem cells?


[User] Please transcribe this text

[Assistant] If you look at them under the microscope, there's not much going on. They're just simple little round cells. But that belies two remarkable attributes.


[User] Please transcribe this text

[Assistant] The second is they can differentiate or become more specialized. So these simple little round cells can turn into skin, can turn into brain, can turn into kidney and so forth.


[User] Please transcribe this text

[Assistant] Grow them up in large numbers, differentiate them into cardiac muscle cells,


[User] Please transcribe this text

[Assistant] Well, for this, we turn to the world of embryology. And for over a century, the embryologists have been pondering the mysteries of heart development. And they had given us what was essentially a Google map for how to go from a single fertilized egg all the way over to a human cardiovascular system.


[User] Please transcribe this text

[Assistant] It took us about five years, but nowadays we can get 90 percent of our stem cells to turn into cardiac muscle. So a 900-fold improvement. So this is quite exciting.


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[Assistant] This slide shows you our current cellular product. We grow our heart muscle cells in little three-dimensional clumps called cardiac organoids. Each of them has 500 to 1,000 heart muscle cells in it.


[User] Please transcribe this text

[Assistant] If you look closely, you can see these little organoids are actually twitching. Each one is beating independently. But they've got another trick up their sleeve.


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[Assistant] But we persevered on this and we came up with a biochemical cocktail that we call our pro-survival cocktail. And this was enough to allow ourselves to survive through this stressful process of transplantation. And now when I look through the microscope, I can see this fresh, young human heart muscle growing back in the injured wall of this rat's heart.


[User] Please transcribe this text

[Assistant] We use these cells essentially like a space probe that we could launch into a foreign environment and then have that flashing report back to us about their biological activity.


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[Assistant] Our cells are flashing, and they're flashing in synchrony back through the walls of the injured heart.


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[Assistant] And if you line up the flashing with the heartbeat that's shown on the bottom, what you can see is there's a perfect one-to-one correspondence. In other words, the guinea pig's natural pacemaker is calling the shots, and the human heart muscle cells are following in lockstep like good soldiers.


[User] Please transcribe this text

[Assistant] So as you look at this, you can see how there's a big deficiency in the muscle in the, in part of the wall of the heart. And it's not hard to imagine how this heart would have a tough time generating much force.


[User] Please transcribe this text

[Assistant] Again, you can see the monkey's heart muscle in red, but it's very hard to even see the blue scar tissue. And that's because we've been able to repopulate it with the human heart muscle, and so we've got this nice plump wall. Okay, let's just take a second and recap.


[User] Please transcribe this text

[Assistant] I've showed you that we can take our stem cells and differentiate them into cardiac muscle. We learned how to keep them alive after transplantation. We showed that they beat in synchrony with the rest of the heart.


[User] Please transcribe this text

[Assistant] These macaque studies also taught us that our human heart muscle cells created a period of electrical instability.


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[Assistant] So our plans at the moment are to make the cells go through this troubled adolescence period while they're still in the dish. And that should, and then we'll transplant them in in the post-adolescent phase, where they should be much more orderly and be ready to listen to their marching orders.


[User] Please transcribe this text

[Assistant] To answer this question, we went to something that's called left ventricular ejection fraction. Ejection fraction is simply the amount of blood that is squeezed out of the chamber of the heart with each beat. Now in healthy macaques, like in healthy people, ejection fractions are about 65 percent.


[User] Please transcribe this text

[Assistant] After a heart attack, ejection fraction drops down to about 40 percent. So these animals are well on their way to heart failure.


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[Assistant] In the animals that receive a placebo injection, when we scan them a month later, we see that ejection fraction is unchanged because the heart, of course, doesn't spontaneously recover.


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[Assistant] Repairing humans is going to be something that is going to go from something that is far-fetched science fiction into common medical practice.


[User] Please transcribe this text

[Assistant] And when this happens, it's going to have a transformational effect that rivals the development of vaccinations and antibiotics. Thank you for your attention.


[User] Please transcribe this text

[Assistant] In this video, we are going to take a look at the importance of analyzing logs.


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[Assistant] The data created in the past two years is more than the data created in the entire history of human race. It is also estimated by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.


[User] Please transcribe this text

[Assistant] Companies who leverage the full power of data analytics could increase their operating margins as much as 60% particularly the retail sector.


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[Assistant] Search. Elastic search was built as a search engine to speed up the search but later on it was extended to other use cases as well as it was used as a data store.


[User] Please transcribe this text

[Assistant] Elasticsearch uses a structure called inverted index which is designed to allow very fast search tricks.


[User] Please transcribe this text

[Assistant] The first use case of the Elastic Stack is Search. Elastic Search was initially built as a search engine to speed up the search. But later on, it was extended to other use cases as well as it was used as a data store. Elastic Search uses a structure called


[User] Please transcribe this text

[Assistant] Inverted index, which is designed to allow very fast full text searches and it is the primary use case of Elasticsearch.


[User] Please transcribe this text

[Assistant] The next use case is suggestions. You could have noticed in many websites like Wikipedia, we could have received suggestions when we type a letter or word in the search tab.


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[Assistant] This autocomplete functionality is also as fast as a user types. The reason behind this is Elasticsearch.


[User] Please transcribe this text

[Assistant] The final use case is log analysis, which will be explained in detail in the upcoming slides. Log analysis. In today's infrastructure, a lot of data is created with which meaningful insights can be derived.


[User] Please transcribe this text

[Assistant] Many organizations aren't matured enough to get the log analyzed, but this could be one of the key things for success in the future. The first and foremost use of log analysis is issue debugging.


[User] Please transcribe this text

[Assistant] Suppose we have an issue being repeated again and again. In the current world, the process could be to correct it manually.


[User] Please transcribe this text

[Assistant] But this can be detected and automated if we have proper log analysis in place. Elastic stack could be used for this purpose.


[User] Please transcribe this text

[Assistant] Next comes the security analysis. For example, we can take the following scenario. User A logs into an application from a system located in London. And the same user, after a few minutes, logs from another system located in India.


[User] Please transcribe this text

[Assistant] This clearly specifies there is a security breach. These kind of security breaches can be


[User] Please transcribe this text

[Assistant] Let us consider a scenario in which we have built a new API and we need to monitor the performance of the API. We might be interested in the number of hits we get and the time taken for the API to respond.


[User] Please transcribe this text

[Assistant] This type of performance analysis can be performed with elastic stack as well. Finally,