[User] Please transcribe this text

[Assistant] So if classifier A has 95% precision, this means that when classifier A says something as a cat, there's a 95% chance it really is a cat.


[User] Please transcribe this text

[Assistant] And recall is, of all the images that really are cats, what percentage?


[User] Please transcribe this text

[Assistant] Uhm, well, we're correctly recognized by your class file. So what percentage of actual cats


[User] Please transcribe this text

[Assistant] So if classifier A has 90% recall, this means that of all the images in say your dev set that really are cats, classifier A accurately pulled out 90% of them.


[User] Please transcribe this text

[Assistant] So don't worry too much about the definitions of precision and recall, but it turns out that there's often a trade-off between precision and recall.


[User] Please transcribe this text

[Assistant] And you care about both. You want that when a classifier says something is a cat, there's a high chance it really is a cat. But of all the images that are of cats, you also want it to pull a large fraction of them as cats.


[User] Please transcribe this text

[Assistant] So it might be reasonable to try to evaluate the classifiers in terms of its precision and its recall.


[User] Please transcribe this text

[Assistant] The problem with using precision recall as your evaluation metric is that if classifier A does better on recall, which it does here, but classifier B does better on precision, then you're not sure which classifier is better.


[User] Please transcribe this text

[Assistant] And if you're trying out a lot of different ideas, a lot of different hyperparameters, you want to really quickly try out not just two classifiers but maybe a dozen classifiers and quickly pick out the quote best ones so you can keep on iterating from there.


[User] Please transcribe this text

[Assistant] And with two evaluation metrics, it's difficult to know how to quickly pick one of the two or quickly pick one of the ten or one.


[User] Please transcribe this text

[Assistant] So what I recommend is rather than using two numbers, precision and recall, to pick a classifier, you instead define a new evaluation metric that combines precision and recall.


[User] Please transcribe this text

[Assistant] In the machine learning literature, the standard way to combine precision and recall is something called an F1 score, and the details of F1 score aren't too important, but informally you can think of this as the


[User] Please transcribe this text

[Assistant] Formally, the F1 score is defined by this formula, 2 over 1 over precision plus 1 over recall. And in mathematics, this is called the, this function is called the harmonic mean of precision P and recall R.


[User] Please transcribe this text

[Assistant] But let's formally think of this as, you know, some way that averages precision and recall.


[User] Please transcribe this text

[Assistant] Uhm, only instead of taking the arithmetic mean, you take the harmonic mean, which is defined by this formula. And it has some advantages in terms of trading off precision and recall. But in this example, you can then see right away that classifier A


[User] Please transcribe this text

[Assistant] As a better F1 score, and assuming F1 score is a reasonable way to combine precision recall, you can then quickly select classifier A over classifier B.


[User] Please transcribe this text

[Assistant] So what I've found for a lot of machine learning teams is that having a well-defined dev set, which is how you're measuring precision recall, plus a single number evaluation metric, sometimes I'll call it single row number evaluation metric,


[User] Please transcribe this text

[Assistant] Allows you to quickly tell if Classify A or Classify B is better.


[User] Please transcribe this text

[Assistant] And therefore having a defset plus single number evaluation metric, this tends to speed up iterating.


[User] Please transcribe this text

[Assistant] Let's say you're building a cat app for cat lovers in four major geographies, you know, the US, China, India, and other, you know, the rest of the world. And let's say that your two classifiers achieve different errors, uhm,


[User] Please transcribe this text

[Assistant] In these, in data from these four different geographies. So LMA achieves 3% error on pictures submitted by US users and so on.


[User] Please transcribe this text

[Assistant] So it might be reasonable to keep track of how well your classifiers do in these different markers or these different geographies, but by tracking four numbers it's very difficult to look at these numbers and quickly decide if algorithm A or algorithm B is superior.


[User] Please transcribe this text

[Assistant] And if you're testing a lot of different classifiers, then it's just difficult to look at all these numbers and quickly pick one. So what I recommend in this example is


[User] Please transcribe this text

[Assistant] In addition to tracking your performance in the four different geographies, to also compute the average, and assuming that average performance is a reasonable single row number evaluation metric,


[User] Please transcribe this text

[Assistant] By computing the average, you can quickly tell that it looks like, uh, algorithm C has the lowest average error and we might then go ahead with that one if you have to pick an algorithm to keep on iterating from.


[User] Please transcribe this text

[Assistant] So your workflow in machine learning is often you have an idea, you implement it and try it out, and you want to know whether your idea helped.


[User] Please transcribe this text

[Assistant] So what we've seen in this video is that having a single number evaluation metric can really improve your efficiency or the efficiency of your team in making those decisions.


[User] Please transcribe this text

[Assistant] Now we're not yet done with the discussion on how to effectively set up evaluation metrics. In the next video, I want to share with you how to set up optimizing as well as satisfying metrics. So let's take a look at the next video.


[User] Please transcribe this text

[Assistant] I'm the mayor of this here town, and I'm responsible for the over 700 types of good, hard-working bacteria living here.


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[Assistant] We spend our days helping to digest foods and protect teeth and gums, but a few bad apples have other ideas. And without Sheriff Brushy here to keep things clean, their plaque is ruining these here teeth.


[User] Please transcribe this text

[Assistant] That's a mighty fine carrot. Was it hard to grow that big?


[User] Please transcribe this text

[Assistant] The data for your supervised learning problem comprises input x and output labels y.


[User] Please transcribe this text

[Assistant] What if you're going through your data and you find that some of these output labels y are incorrect? That you have data which is incorrectly labeled. Is it worth your while to go in to fix up some of these labels? Let's take a look.


[User] Please transcribe this text

[Assistant] In the cat classification problem, y equals one for cats and zero for non-cats. So let's say you're looking through some data and that's a cat, that's not a cat, that's a cat, that's a cat, that's not a cat, that's a cat.


[User] Please transcribe this text

[Assistant] No wait, that's actually not a cat, so this...


[User] Please transcribe this text

[Assistant] Is an example with an incorrect label.


[User] Please transcribe this text

[Assistant] So I've used the term mislabeled examples to refer to if your learning algorithm outputs the wrong value of y.


[User] Please transcribe this text

[Assistant] But I'm going to say incorrectly labeled examples when to refer to if in the data set you have, in the training set or the dev set or the test set, the label for Y, whatever human label assigned to this piece of data is actually incorrect. And that's actually a dog, so that Y really should have been zero. But maybe the labeler got that one wrong.


[User] Please transcribe this text

[Assistant] So if you find that your data has some incorrectly labeled examples, what should you do? Well,


[User] Please transcribe this text

[Assistant] First, let's consider the training set.


[User] Please transcribe this text

[Assistant] It turns out that deep learning algorithms are quite robust to random errors in the training set. So, so long as your errors or your incorrectly labeled examples, so long as those errors are not too far from random, you know, maybe sometimes, uhm,


[User] Please transcribe this text

[Assistant] If the errors are reasonably random, then it's probably okay to just leave the errors as they are and not spend too much time fixing them.


[User] Please transcribe this text

[Assistant] There's certainly no harm to going into your trading set and re-examining the labels and fixing them. Sometimes that is worth doing. But your algorithm might be okay even if you don't. So long as the total data set size is big enough.


[User] Please transcribe this text

[Assistant] And the actual percentage of errors is, you know, maybe not too high.


[User] Please transcribe this text

[Assistant] So I see a lot of machine learning algorithms that are trained even when we know that there are a few mistakes in the training set labels and usually it works okay. There is one caveat to this, which is that


[User] Please transcribe this text

[Assistant] Deep learning algorithms are robust to random errors. They are less robust to systematic errors.


[User] Please transcribe this text

[Assistant] So for example, if your label is consistently labels white dogs as cats, then that is a problem because your classifier will learn to classify all white colored dogs as cats.


[User] Please transcribe this text

[Assistant] But random errors or near random errors are usually not too bad for most deep learning algorithms.


[User] Please transcribe this text

[Assistant] Now, this discussion has focused on what to do about incorrectly labeled examples in your training set.


[User] Please transcribe this text

[Assistant] How about incorrectly labeled examples in your dev set or test set? If you're worried about the impact of incorrectly labeled examples on your dev set or test set, what I recommend you do is, during error analysis, to add one extra column


[User] Please transcribe this text

[Assistant] So that you can also count up the number of examples where the label y was incorrect.


[User] Please transcribe this text

[Assistant] So for example, maybe when you count up the impact on a hundred mislabeled defset examples, so you're going to find a hundred examples where your classifies output disagrees with the label in your defset.


[User] Please transcribe this text

[Assistant] And sometimes, for a few of those examples, your classifier disagrees with the label because the label was wrong, rather than because your classifier was wrong. So maybe in this example, you find that the labeler missed a cat in the background, so uhm,


[User] Please transcribe this text

[Assistant] Let's put a check mark there to signify that example 98 had an incorrect label. And maybe for this one,


[User] Please transcribe this text

[Assistant] The picture is actually a picture of a drawing of a cat rather than a real cat and maybe you want the laborer to have labeled that y equals zero rather than y equals one.


[User] Please transcribe this text

[Assistant] You put another check mark there. And just as you count up the percent of errors due to other categories like we saw in the previous video, you'd also count up the fraction of percentage of errors due to incorrect labels.


[User] Please transcribe this text

[Assistant] Where the y value in your dev set was wrong and that accounted for why your learning algorithm made a prediction that differed from what the label on your data says.


[User] Please transcribe this text

[Assistant] So the question now is, is it worthwhile going in to try to fix up, you know, this 6% of incorrectly labeled examples?


[User] Please transcribe this text

[Assistant] My advice is, if it makes a significant difference to your ability to evaluate algorithms on your dev set, then go ahead and spend the time to fix the incorrect labels. But if it doesn't make a significant difference to your ability to use the dev set to evaluate classifiers, then it might not be the best use of your time.


[User] Please transcribe this text

[Assistant] Let me show you an example that illustrates what I mean by this.


[User] Please transcribe this text

[Assistant] So three numbers I recommend you look at to try to decide if it's worth going in and reducing the number of mislabeled examples are the following. I recommend you look at the overall defset error.


[User] Please transcribe this text

[Assistant] And so in the example we had from the previous video, we said that maybe your system has 90% overall accuracy, so 10% error. Then you should look at the number of errors of the system.


[User] Please transcribe this text

[Assistant] Percentage of errors that are due to incorrect labels. So it looks like in this case, 6% of the errors are due to incorrect labels. So 6% of 10% is 0.6%. And then you should look at errors due to all other causes. So,


[User] Please transcribe this text

[Assistant] There's 9.4% worth of error that you could focus on fixing, whereas


[User] Please transcribe this text

[Assistant] You know, the errors due to incorrect labels is a relatively small fraction of the overall set of errors. So by all means go in and fix these incorrect labels if you want, but this may be not the most important thing to do right now.


[User] Please transcribe this text

[Assistant] Now, let's take another example. Suppose you've made a lot more progress on your learning problem. So instead of 10% error, let's say you brought the errors down to 2%. But still,


[User] Please transcribe this text

[Assistant] Uhm, 0.6% of your overall errors are due to incorrect labels.


[User] Please transcribe this text

[Assistant] So now, if we were to examine a set of mislabeled depth set images, so set that comes from this 2% of depth set data you're mislabeling, then


[User] Please transcribe this text

[Assistant] A lot, a very large fraction of them, 0.6.


[User] Please transcribe this text

[Assistant] Uh, divided by 2%, so that's actually 30% rather than 6% of your labels, uhm,


[User] Please transcribe this text

[Assistant] Mistakes, at least as measured on your dev set, are due to incorrect labels, then it maybe seems much more worthwhile to fix up the incorrect labels in your dev set.


[User] Please transcribe this text

[Assistant] And if you remember the goal of the dev set, the main purpose of the dev set is you want to really use it to help you select between two classifiers, A and B.


[User] Please transcribe this text

[Assistant] So if you're trying out two class files, A and B, and one has 2.0,


[User] Please transcribe this text

[Assistant] 1% error and the other has 1.9% error on your devset. But you don't trust your devset anymore to be correctly telling you whether this classifier is actually better than this because 0.6% of these mistakes are due to incorrect labels.


[User] Please transcribe this text

[Assistant] Then that's a good reason to go in and fix the incorrect labels in your devset.


[User] Please transcribe this text

[Assistant] Because in this example on the right, it's just having a very large impact on the overall assessments of the errors of the algorithm. Whereas the example on the left,


[User] Please transcribe this text

[Assistant] Now, if you decide to go into your dev set and manually re-examine the labels and try to fix up some of the labels, here are a few additional guidelines or principles to consider.


[User] Please transcribe this text

[Assistant] I would encourage you to apply whatever process you apply to both your deaf and test sets at the same time.


[User] Please transcribe this text

[Assistant] We've talked previously about why you want your dev and test sets to come from the same distribution. The dev set is sort of telling you where to aim the target, and when you hit it, you want that to generalize to the test set. So your team really works more efficiently if the dev and test sets come from the same distribution.


[User] Please transcribe this text

[Assistant] So if you're going in to fix up your dev set, I would apply the same process to the test set to make sure that they continue to come from the same distribution. So if you hire someone to examine the labels more carefully, do that for both your dev and test sets.


[User] Please transcribe this text

[Assistant] Second, I would urge you to consider examining examples your algorithm got right as well as ones it got wrong. It is easy to look at the examples your algorithm got wrong and just see if any of those need to be fixed.


[User] Please transcribe this text

[Assistant] But it's possible that there's some examples that your algorithms got right that should also be fixed. And if you only fix ones that your algorithms got wrong, you end up with a more biased estimate of the error of your algorithm.


[User] Please transcribe this text

[Assistant] It kind of gives your algorithm a little bit of an unfair advantage if you just try to double check what it got wrong, but you don't also double check what it got right, because it might have gotten something right.


[User] Please transcribe this text

[Assistant] That it was, you know, just lucky on fixing the label.


[User] Please transcribe this text

[Assistant] The second bullet isn't always easy to do, so it's not always done. Uhm, the reason it's not always done is because if your classifier is very accurate, then it's getting a lot fewer things wrong than right. So if your classifier has your 98% accuracy, then


[User] Please transcribe this text

[Assistant] It's getting 2% of things wrong and 98% of things right. So it's much easier to examine and validate the labels on 2% of the data. And it takes much longer to validate the labels on 98% of the data. So this isn't always done, but it's just something to consider.


[User] Please transcribe this text

[Assistant] Finally, if you go into your dev and test data to correct some of the labels there,


[User] Please transcribe this text

[Assistant] You may or may not decide to go and apply the same process for the training set. Remember we said at the start of this video that it's actually less important to correct the labels in your training set. And it's quite possible you decide to just correct the labels in your dev and test set, which are also often smaller than the training set.


[User] Please transcribe this text

[Assistant] And you might not invest all that extra effort needed to correct the labels in the much larger training set.


[User] Please transcribe this text

[Assistant] This is actually okay, and we'll talk later this week about some processes for handling when your training data is different in distribution than your dev and test data. Learning albums are quite robust to that.


[User] Please transcribe this text

[Assistant] It's super important that your dev and test sets come from the same distribution.


[User] Please transcribe this text

[Assistant] But if your training set comes from a slightly different distribution, often that's a pretty reasonable thing to do. And we'll talk more about how to handle this later this week.


[User] Please transcribe this text

[Assistant] So I'd like to wrap up with just a couple pieces of advice. First, deep learning researchers sometimes like to say things like, oh, I just fed the data to the algorithm and I trained it and it worked. And you know, there is a lot of truth to that. In the deep learning era, there is more of feeding data to an algorithm and just training it and doing less hand engineering and using less human insight. But I think that


[User] Please transcribe this text

[Assistant] In building practical systems often there's also more manual error analysis and more human insight that goes into these systems and sometimes deep learning researchers like to acknowledge.


[User] Please transcribe this text

[Assistant] Uhm, second is that somehow I've seen some engineers and researchers be reluctant to manually look at examples. Maybe it's not the most interesting thing to do, to sit down and look at a hundred or a couple hundred examples to counter the number of errors.


[User] Please transcribe this text

[Assistant] But this is something that I still do myself when I'm leading a machine learning team and I want to understand what mistakes it's making. I will actually go in and look at the data myself and try to counter.


[User] Please transcribe this text

[Assistant] The fraction of errors and I think that because these, you know, minutes or maybe a small number of hours of counting data can really help you prioritize where to go next. I find this a very good use of your time and I urge you to consider doing it if you've built machine learning system and you're trying to decide.


[User] Please transcribe this text

[Assistant] Ideas without directions to prioritize things.


[User] Please transcribe this text

[Assistant] So uhm, that's it for the error analysis process. In the next video, I want to sort of share with you some thoughts on how error analysis fits into how you might go about starting out on a new machine learning project.


[User] Please transcribe this text

[Assistant] A young man in Tehran, saved up for years to buy a real bed. For the first time in his life.


[User] Please transcribe this text

[Assistant] He became the proud owner of a bed which had springs and a mattress.


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[Assistant] He carried the bed onto the roof of his house.


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[Assistant] He slept very well for the first two nights, but on the third night a storm blew up.


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[Assistant] A gust of wind swept the bed off the roof.


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[Assistant] And sent it crashing into the courtyard below.


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[Assistant] The young man did not wake up until the bed had struck the ground.


[User] Please transcribe this text

[Assistant] Although the bed was smashed to pieces, the man was miraculously unhurt.


[User] Please transcribe this text

[Assistant] When he woke up, he was still on the mattress.


[User] Please transcribe this text

[Assistant] Glancing at the bits of wood and metal that lay around him.


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[Assistant] The man sadly picked up the mattress and carried it into his house.


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[Assistant] After he had put it on the floor, he promptly went to sleep again.


[User] Please transcribe this text

[Assistant] Hello there, my name is Richard McMunn from the interview training company, PassMyInterview.com. And in this tutorial, I'm going to teach you how to pass a nursing interview. And in particular, I'm going to give you some tough nursing interview questions and more importantly, some high scoring answers. So if you have


[User] Please transcribe this text

[Assistant] Any kind of nursing interview coming up whatsoever, please do watch this from beginning to end, because I believe the content, the tips and the answers are going to really make a difference to your


[User] Please transcribe this text

[Assistant] Preparation and your performance at the interview. Now, before I get into the all-important nursing interview questions and answers, a very warm welcome to this tutorial.


[User] Please transcribe this text

[Assistant] My name is Richard McMunn. That's me there in the center. And I've been helping people to pass their interviews for over 20 years now. I love what I do. I'm very passionate about it. And I enjoy creating the tips and the answers.


[User] Please transcribe this text

[Assistant] Before I get into those, please do make sure you SUBSCRIBE to the channel by clicking that all-important red button below the video. That way you won't miss out on any of the weekly interview training videos that I am uploading. And also, if you enjoy the content, I would very much appreciate it if you gave the video a LIKE. Thank you very much. Now, let's get straight into those nursing interview questions and answers.


[User] Please transcribe this text

[Assistant] The next question that you will get asked during your nursing interview is tell us about yourself and why you think you will make a competent nurse.


[User] Please transcribe this text

[Assistant] Okay, so this answer is based for anybody, whether you have any prior experience of being a nurse or you're just going for your first nursing position, this response will help you. Okay, here we go.


[User] Please transcribe this text

[Assistant] First and foremost, I am totally dedicated to patient care.


[User] Please transcribe this text

[Assistant] And I find this element of the role the most rewarding. I am naturally a caring person and I enjoy being in a role where I get the opportunity to make a difference through both the treatment and care I provide and also through educating patients


[User] Please transcribe this text

[Assistant] I am able to follow strict rules and procedures. I have a passion to continually learn and


[User] Please transcribe this text

[Assistant] And I'm also someone who can work hard under pressure and respond positively to the challenges the nursing role presents itself. I feel confident that if you employ me within this nursing position, you will be impressed with my strong work ethics and my ability to contribute positively to the nursing team. So that's a pretty solid answer.


[User] Please transcribe this text

[Assistant] Now, you're probably thinking, where can I get a copy of these questions and answers? Please do carry on watching the video, because I'll tell you where you can download a copy of these and an entire bunch of 21


[User] Please transcribe this text

[Assistant] Nursing interview questions and answers as I progress through the tutorial. Okay, let's move on to the second question of your nursing interview. Tell me what you know about the role of a nurse.


[User] Please transcribe this text

[Assistant] As a nurse, I will be required to carry out many tasks, including the assessment, the planning and the evaluation of patient's condition, their care and their treatment. I would need to maintain confidentiality at all times, keep accurate records and also follow strict policies, rules and operational protocols.


[User] Please transcribe this text

[Assistant] Further responsibilities include keeping a safe environment for patients and also prioritizing patient care and treatment


[User] Please transcribe this text

[Assistant] I will need to always carry out high standards of quality nursing care, abide by the 60s of nursing and follow the organization's values at all times whilst helping to develop and implement national initiatives aimed at improving quality of care, standards and efficiency. And that's a great answer.


[User] Please transcribe this text

[Assistant] And all that comes with the job. So how do you handle the stress of being a nurse and all that comes with the job? Here we go. Here's my sample answer.


[User] Please transcribe this text

[Assistant] The environment you'll be working in and also the situation most patients will find themselves in. The nursing role requires you to work in a pressurized environment whereby people are often anxious and under stress based on their illness or their upcoming treatment. On that basis,


[User] Please transcribe this text

[Assistant] You have to understand that people are not their normal self, and you have to take this into account when helping them, caring for them and also treating them.


[User] Please transcribe this text

[Assistant] I naturally have a calm nature and I will not be put off by stressful situations whereby I have to perform under pressure.


[User] Please transcribe this text

[Assistant] I can handle the stress of the job because I believe I am a strong communicator who has the confidence in my own abilities. The truth is I would rather work in a pressurized and stressful environment


[User] Please transcribe this text

[Assistant] Because, because it is times like these where I can really put my skills and experiences to good use. So that's another solid answer.


[User] Please transcribe this text

[Assistant] Now I'm going to give you another interview question and answer in a second. But if you want to download all 21 of my nursing interview questions and high scoring answers, if you click the link that's appeared right there in the top right hand corner, it will take you through to my website where you can download all 21.


[User] Please transcribe this text

[Assistant] And which parts will be the most challenging? So this is assessing your knowledge of what the role will be like, both the good parts and also the difficult and challenging parts. So here's my suggested answer to this question.


[User] Please transcribe this text

[Assistant] Although some of the patients I will deal with will be under pressure and stress, I feel the most enjoyable aspect will be seeing them get better and also helping them to lead more healthier lives through education and information. I also feel I will find working with the nursing team an enjoyable aspect to the role.


[User] Please transcribe this text

[Assistant] As you will get to build long lasting relationships with people whom you can trust and rely on when you are working under pressure within the role of a nurse. In respect to the most challenging, this will be when dealing with patients who are under the influence of drugs or alcohol, and also those patients who refuse to take responsibility for their own health.


[User] Please transcribe this text

[Assistant] Whilst these will be the most challenging aspects of the nursing role, I will always look upon these as an opportunity to put my people skills and communication skills to the test.


[User] Please transcribe this text

[Assistant] I am a confident person who can deal with the difficult and challenging aspects of the nursing role, and I will always carry out my duties with a positive attitude and a professional approach.


[User] Please transcribe this text

[Assistant] And that is a strong answer to that question and demonstrates that, you know, what will be the most enjoyable parts that you can put your skills to the test, but also the challenging ones, but that you have a positive mindset when dealing with those as well. Now, don't forget, if you want to download all 21 nursing interview questions and answers, you can click the link below the video right there.


[User] Please transcribe this text

[Assistant] Great in these interview questions and answers for you. And I wish you all the best in your pursuit to passing your nursing interview. Thank you very much for watching.


[User] Please transcribe this text

[Assistant] When you're an amphibian, your home is in the water and on land, on lake shores.


[User] Please transcribe this text

[Assistant] I'm Jake, the Supreme Cupcake. Oh yeaah.


[User] Please transcribe this text

[Assistant] You cats don't believe me? I can prove it to ya. All you gotta do is climb inside. Alright!


[User] Please transcribe this text

[Assistant] Buckle up, storybots, it's tummy time!


[User] Please transcribe this text

[Assistant] Through myself, then Inkus, and finally Stapies, the beat of the drum passes on through the oval window.


[User] Please transcribe this text

[Assistant] Hello there, my name is Richard McMunn from the interview training company, PassMyInterview.com. And in this tutorial, I'm going to teach you how to answer the university admissions interview question, why did you choose this university? So it does not matter which university you are hoping to join.


[User] Please transcribe this text

[Assistant] If you have an admissions interview coming up, you will need to answer this question. Why did you choose this university? And in this presentation, to help you answer it, this is what I'm going to cover.


[User] Please transcribe this text

[Assistant] I am going to tell you the four things you must include in your answer. These are very important. I will tell you the exact keywords and phrases I recommend you use in your response.


[User] Please transcribe this text

[Assistant] I will tell you what the university interview panel want to hear from your answer. And I will then give you two brilliant sample answers to the question. Why did you choose this university to help you pass your admissions interview? So there's four reasons why you should stay tuned and let's get straight into


[User] Please transcribe this text

[Assistant] They want to hear that you are going to dedicate your time at university to quality study, learning and development.


[User] Please transcribe this text

[Assistant] They want to hear that you have considered carefully their university and that you have genuine reasons for studying there. And number four, they want to hear that you understand how much work is involved and that you will be an organized and committed student.


[User] Please transcribe this text

[Assistant] So those are the four things we are going to consider when creating our answer to this question.


[User] Please transcribe this text

[Assistant] Now, three important tips before I give you two sample answers to the university admissions interview question. Why did you choose this university? Tip number one, be positive and enthusiastic in your answer. Show that you really want to study there. Tip number two,


[User] Please transcribe this text

[Assistant] Show that you've carried out some research into their university, you've studied online, perhaps you've spoken to other students and you have also taken the time to study in detail the contents of the program


[User] Please transcribe this text

[Assistant] You are hoping to study there and tip number three, make sure you align your future career goals to studying at their university. And I'll show you what I mean by that.


[User] Please transcribe this text

[Assistant] In the answer that is coming right now. So here's two brilliant answers to the interview question. Why did you choose this university? Here's the first one.


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[Assistant] I have chosen this university for four reasons. Number one, I carried out extensive research into the university, your track record of achievement and the support you provide for your students.


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[Assistant] During my research, everything pointed to this being a positive, supportive and encouraging place for me to learn and study.


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[Assistant] Number two, it is evident that the Vice Chancellor, the lecturers and the heads of departments want to create an environment that is conducive to quality education. And I feel this university is the place I will get the most out of my time whilst I am here. Number three,


[User] Please transcribe this text

[Assistant] There are many universities offering this program. However, having studied the program content closely, I believe the depth of knowledge I will gain in respect of the subject matter, the level of expertise and knowledge that lecturers have and the wider communication and interpersonal skills I will gain make it my number one choice. Finally,


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[Assistant] I have chosen this university because I believe my long-term career goals can only be achieved if my time at university is not only highly productive, but to a standard that is respected by the industry I want to work in.


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[Assistant] This university is the only place I want to study and I give you my assurances that I will be a positive role model and someone who is supportive of other students too." So that is a brilliant answer to the interview question.


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[Assistant] Why did you choose this university? Now, I'm going to give you another


[User] Please transcribe this text

[Assistant] Sample answer in a second. But if you want to click that link that's appeared in the top right hand corner of the video, you can go through to my website, PassMyInterview.com, where you can download these answers, these exact answers, plus other university admissions interview questions and brilliant answers too. It's in PDF format. But here's the next


[User] Please transcribe this text

[Assistant] Sample answer to the question why did you choose this university? Here we go. Prior to applying to study at this university,


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[Assistant] I spent a considerable amount of time researching the facilities and the standard of tuition on offer.


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[Assistant] The university ticks all the boxes regarding what I am looking for from my education and the environment I wish to study in.


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[Assistant] I spoke to several students who have already studied at this university and they all had positive things to say. Both the standards of teaching and the passion the lecturers have for their subject matter were just two highlights from the feedback I received


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[Assistant] From my research, it also became apparent that the university genuinely cares about the progress and development of its students, and I want to spend my time in a positive environment where I will be given every opportunity to succeed. In addition to studying this


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[Assistant] Again, I believe I will get all of this whilst studying at this university and I give you my assurances that, if I am fortunate enough to be offered a place here, I will work hard to achieve my goals whilst being a positive role model both on and off campus.


[User] Please transcribe this text

[Assistant] That is another top scoring answer, a really strong positive one.


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[Assistant] So the next thing to do is please CLICK THAT LINK in the top right hand corner of the video


[User] Please transcribe this text

[Assistant] Go through to my website, PassMyInterview.com and you can download the two answers, exact answers and other


[User] Please transcribe this text

[Assistant] Top scoring answers to common university interview questions in PDF format. I hope you've enjoyed that. Please make sure you SUBSCRIBE to my channel. And don't forget to HIT THE LIKE BUTTON to show your support for the content I'm creating. Thank you very much for watching.


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[Assistant] And I wish you all the best for passing your university admissions interview. Have a brilliant day!


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[Assistant] And, uh, and so these are the principles that we have layered in under Meg's five top principles.


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[Assistant] And kind of what I want to do is just talk about the high level ones. We power the business.


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[Assistant] And when you are one of the two levers, you take that job pretty seriously. And so we do power the business and we'll talk a little bit about, about the responsibility when you're in that kind of engineering organization. The next one is we are chromatic and focused on value.


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[Assistant] Once again, time to market is extremely important. We don't, we do not design technology for the sake of technology. We want to make sure it's flexible. We want to make sure it's built for the future, but we want to make sure that we understand that, that what we do is enabling our business and every single thing we should do to keep that in mind.


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[Assistant] We continuously improve. We'll talk a little bit more about that. But when you're one of the major two cost factors of the company, you want to continue to get better. And we'll give some examples of that. We're also part of the eBay team.


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[Assistant] But, uh, so that means that every single person is part of that same product. So you can't work in a vacuum. You actually have to work across the business unit, legal, customer support, community.


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[Assistant] Lesson 50. Taken for a ride.


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[Assistant] I love travelling in the country, but I don't like losing my way.


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[Assistant] I went on an excursion recently, but my trip took me longer than I expected.


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[Assistant] I'm going to Woodford Green, I said to the conductor as I got on the bus. But I don't know where it is. I'll tell you where to get off, answered the conductor.


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[Assistant] I realized with a shock that I was the only passenger left on the bus. You'll have to get off here, the conductor said. This is as far as we go.


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[Assistant] Is this Woodford Green? I asked. Oh dear. Said the conductor suddenly. I forgot to put you off.


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[Assistant] It doesn't matter, I said. I'll get off here.


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[Assistant] Well, in that case, I prefer to stay on the bus, I answered.


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[Assistant] Always had some type of ball around him and he just gravitated toward the game. I never had to say, all right, this is basketball. This is what you're going to play, son. He's always loved the game and hopefully it was because he was able to watch Pops do it a little bit.


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[Assistant] It was a blessing to have a son and then to have him grow up and watch his dad, he got upset when he couldn't. So we kind of used that, you know, as motivation to, hey, you know, do you, do you, do well in school, behave, do your chores and you can go to practice and hang out.


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[Assistant] Boy, that's gonna take a lot of hard work and practice.


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[Assistant] He was playing AAU baseball and basketball. And I was coaching both teams.


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[Assistant] Uh, than I ever imagined he would be or that I got to, so.


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[Assistant] My wife and I were always pinching ourselves, you know, can we believe that's our kid?


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[Assistant] Get some longer clubs or something. He swings like he shoots three pointers. Smooth. Nice and smooth. Good shot. Nice result as well on the green.


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[Assistant] Now we're like 30 year old parents and it really has flown by. It's almost one of those things where like we need to take a step back at some point just to sit down and like breathe and


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[Assistant] Take a look at everything that's happened because I think, I feel, I almost feel like everybody's more aware of what's gone on than we are.


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[Assistant] I can't believe all of the success Stefan has had in his career with the team and personally. But at the same time, it's because like the grind never stops.


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[Assistant] Seeing his work ethic everyday, you can't help but to like, want to try and work half as hard as he does.


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[Assistant] And, uh, build times have gone from two hours to actually six hours and six hours plus. When you have 200 engineers,


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[Assistant] And you're doing about 1,400 builds a week. That's a lot of inefficiency.


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[Assistant] We, we got down to two hours, not for every build, but we figured out how to do it. And then in the next quarter, we actually productionalized it so that the majority of the builds happened within that two hours. What I've said is, that's not, no, that's great. We got back to where we should have been. Now let's get down to 30 minutes and 20 minutes.


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[Assistant] And that is just one example. We probably have about 15 examples right now of significant programs that we have in engineering actually to continually improve, to do what we call continually raising the bar.


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[Assistant] Ha man, the guys, you tell me you haven't seen them at commercial? They run all over town, you can't miss it. Uh, alright, look, I'll show you.


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[Assistant] Great, yeaah. Okay, so what are you story buds interested in today? I got a toaster oven. I got a microwave. You want cell phone? I got, I got smartphone. I got dumb phone.


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[Assistant] I don't know Pierce, but I like the name Pierce. What would Pierce want to know? He wants to know where electricity comes from. Electricity? Ah, that's so easy.


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[Assistant] You see, you take the plug, you find the outlet, you put the plug into the outlet, and then


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[Assistant] The electricity come from the outlet and through the wire to the device.


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[Assistant] Electricity, it may be very great for turning things on, but also can be very dangerous.


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[Assistant] If you get too close to one of these, it can hurt you, end up in the hospital, or even worse.


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[Assistant] Yeah, it's, uh, it's actually, to some extent it was, uh, I started in deep learning to some extent by luck.


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[Assistant] Uhm, I did my master's degree at Toronto and then I took a year off. I was actually working in the financial sector. It's a little bit surprising. Uhm, and at that time I wasn't quite sure whether I want to go for my PhD or not and then something happened, something surprising happened. I was going to work one morning and I bumped into Jeff Hinton.


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[Assistant] Come to my office, I'll show you. And so we basically walked together and he started telling me about, you know, these Boltzmann machines and contrastive divergence and some of the tricks which I didn't at that time quite understand what he was talking about.


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[Assistant] Uh, but that really, really excited, that was very exciting, uh, and really excited me. And then basically within three months, uh, I started my PhD.


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[Assistant] With Jeff, uh, so, so that was, uh, that was kind of like the beginning because that was back in 2005, 2006, and this is where, you know, some of the original, uh, deep learning algorithms using restricted boss machines and supervised training were kind of


[User] Please transcribe this text

[Assistant] Uh, uh, popping up and so, you know, that's, that's how I started. It was really, you know, uh, that one particular morning when I bumped into Jeff, completely changed my, uh, my, uh,


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[Assistant] And, uh, Jeff and I were trying to explore these ideas of using restricted boss machines and using pre-training tricks to train multiple layers.


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[Assistant] And specifically we will try to focus on auto-encoders. You know, how do we do a non-linear extension of PCA, uh, effectively.


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[Assistant] And it was very exciting because we got these systems to work on MNIST digits, which was exciting, but then the next steps for us were to really see whether we can extend these models to dealing with faces. I remember we had, uh, this, uh, Olivetti faces dataset.


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[Assistant] That was super exciting, yeaah, because it was a lot of learning for me, uh, but at the same time the results turned out to be...


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[Assistant] You sort of can imagine training a stack of these restricted balls machines that would allow you to learn effectively one layer at a time. And there's a good theory behind, you know, when you add a particular layer, it improves the variation bound and so forth under certain conditions. So there was a theoretical justification and these models were working quite well.


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[Assistant] In terms of being able to pre-train these systems. And then around 2009, 2010, once the computers started showing up, you know, GPUs,


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[Assistant] Uhm, then a lot of us, uh, started realizing that actually directly optimizing these deep neural networks was, you know, was giving, uh, similar results or even better results.


[User] Please transcribe this text

[Assistant] That's right, that's right. And that's sort of over, you know, three or four years and it was exciting to the whole community because people thought that wow, you can actually train these deep models using these pre-training mechanisms.


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[Assistant] Uhm, and then, you know, with more compute, people started realizing that you can just basically do standing back propagation. Something that we couldn't do back in 2005 or, you know, 2004 because it would take us months to do it on CPUs. And so that was, uhm, that was a big change.


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[Assistant] Uh, the other thing that I think that we haven't really figured out what to do with, uh, you know, both machines and deep both machines, I believe they're very powerful models because you can think of them as generative models. Uh, you know, they try to model complex distributions in the data.


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[Assistant] But when we start looking at learning algorithms, uh, learning algorithms right now, they require using, you know, Markov Chain Monte Carlo and variational learning and such, which is not as scalable as back propagation algorithms. So, uh, so we yet have to figure out more efficient ways of


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[Assistant] Yeah, I think that's a, that's a really, I feel like it's a very important topic particularly if we think about unsupervised or semi-supervised or generative, generative models because


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[Assistant] Uhm, to some extent a lot of successes that we've seen very recently is due to supervised learning. And back in the early days, uhm, unsupervised learning was, was primarily viewed as unsupervised pre-training because we didn't know how to train these multi-layer


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[Assistant] Uh, systems, and even today, if you're working, uh, in settings where you have lots and lots of unlabeled data, and a small fraction of labeled examples, you know, these unsupervised pre-training models, building these generative models can help.


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[Assistant] Uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm, Uhm,


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[Assistant] There is a lot of work right now on generative modeling, uh, you know, if you look at generative adversarial networks, if you look at variational autoencoders, deep energy models is something that my lab is working on right now, uh, as well.


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[Assistant] I think it's, it's very exciting research, but we haven't, perhaps we haven't quite figured it out. Again, for many of you who are thinking about getting into the deploying field, this is one area that I think we, you know, we'll make a lot of progress in, hopefully in the near future.


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[Assistant] You know, all the work on Bolson machines, variational encoders, GANs, you can think a lot of these models as generative models, but we haven't quite figured out, you know, how to, you know, really make them work.


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[Assistant] You know, companies have lots and lots of data, lots of unlabeled data. There's lots of efforts for going through annotations, uh, cause that's the only way for us to, uh, to make progress right now. And it seems like, you know, we should be able to, uh, make use of unlabeled data cause it's, you know, it's just abundance of it. And we haven't quite figured out how to do that yet.


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[Assistant] People entering that field, I would encourage them to just try different things and not be afraid to try new things and not be afraid to try to innovate. I can give you one example which is when I was a graduate student.


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[Assistant] And the feedback was always that, well, there is no way you can solve these problems because these are non comics, we don't understand optimization. How could you ever even do that? Uh, you know, compared to doing comics optimization. And it was surprising because in our lab,


[User] Please transcribe this text

[Assistant] You know, we never really cared that much about those specific problems. We just, we're thinking about how can we optimize and whether we can get interesting results. And that effectively was driving, uh, the community. So we were not, we're, you know, we were not scared.


[User] Please transcribe this text

[Assistant] Maybe to some extent because we didn't, maybe because we were lacking actually the theory behind optimization. Uhm, but, but I would encourage people to just try and not be afraid to try to tackle hard problems.


[User] Please transcribe this text

[Assistant] Yes, that's right. I think that, look, it's one of the things that I try to do when I teach a deep learning class is, is, is one of the, for one of the homeworks, I'm asking people to actually code back propagation algorithm for convolutional neural networks.


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[Assistant] And it's, you know, it's painful, uh, but at the same time, if you do it once, you really understand how these systems operate and how they work.


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[Assistant] Uh, and how you can efficiently implement them on, on, on GPU. And I think it's, it's important for you to, uh, uh, when you go into research or industry, you have a really good understanding of what these systems are doing. Uhm, so it's, it's important, I think, you know.


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[Assistant] Yeah, I think that's actually a very good question. Uhm, in my particular lab, I have a mix of students. Some students want to go and take an academic route. Some students want to go and take an industry route. And it's, uhm, it's becoming very challenging because


[User] Please transcribe this text

[Assistant] Uhm, in academia I feel like you have more freedom.


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[Assistant] To work on long term, uh, problems. Or if you think about some crazy problem, you can work on it.


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[Assistant] Uh, so you have a little bit more freedom. At the same time, the research that you do in industry is also very exciting because in many cases you can


[User] Please transcribe this text

[Assistant] With your research you can impact millions of users if you develop, you know, a core AI technology. And obviously within the industry you have much more resources in terms of compute and be able to, you know, do really amazing things.


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[Assistant] One particular area that I think is really exciting is the area of deep reinforcement learning.


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[Assistant] Because we were able to figure out how we can train agents in virtual worlds and this is something that's, uh, in just the last couple of years you see a lot, a lot of progress of how can we scale these systems, how can we develop new algorithms, how can we get agents to communicate to each other.


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[Assistant] Uhm, so can we build dialogue-based systems? Can we build systems that can reason, that can, uh, read text and be able to, you know, answer questions intelligently? I think this is something that a lot of research is focusing on right now.


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[Assistant] Uhm, and then uhm, there's another sort of sub area also, is this area of being able to learn from fewer examples. So typically, you know, people think of it as one-shot learning or transfer learning, a setting where, you know, you, you,


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[Assistant] Uh, and so this is something that's, uh, a lot of us in the community are trying to figure out how we can, how we can do that and how can we sort of come closer to human-like, uh,


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[Assistant] Not all that much bigger than a bee. It's the smallest bird in the world.


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[Assistant] There are all sorts of little castaways that have found their way to these islands. Many are the tiniest of their kind and found nowhere else on earth.


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[Assistant] Food and space is so limited on islands. It's often survival of the smallest.


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[Assistant] On sunny days, life on a Caribbean island is buzzing with possibilities.


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[Assistant] A male bee hummingbird looking for a mate.


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[Assistant] She's caring for two chicks, just hatched from eggs the size of coffee beans into a home the size of an espresso cup.


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[Assistant] And as more swarm in, the chicks are in serious danger.


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[Assistant] shrinking down to make the most of what little food they can find.


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[Assistant] Adult ants are too large to get his tiny jaws around. But their larvae go down nicely.


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[Assistant] The dwarf gecko is one of the smallest lizards in the world.


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[Assistant] The perfect size to hunt micro bugs down here in the leaf litter.


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[Assistant] But the storm is so overwhelming. There's no choice but to abandon her nest.


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[Assistant] On the forest floor, the runoff becomes a tidal wave.


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[Assistant] What size is a dwarf gecko's superpower?


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[Assistant] With over 15 tropical storms battering the Caribbean each year, paradise can quickly turn into hell.


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[Assistant] She must start all over again before the next storm hits.


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[Assistant] In the wake of a storm, vegetation is washed out to sea. And with it, creatures are cast adrift.


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[Assistant] Occasionally, a voyager is lucky enough to reach new shores.


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[Assistant] This is how many animals colonize new islands.


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[Assistant] Small and hearty, Anolis lizards make successful stowaways.


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[Assistant] Hoodies and iguanas survive by scavenging on whatever washes up.


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[Assistant] Just a few meters. Between her and safety.


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[Assistant] It only takes a few brave pioneers to start a whole new population.


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[Assistant] The storm has delivered a bounty for beachcombers of all sizes. From hoodia to sandhoppers.


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[Assistant] But not everyone's on the hunt for food. This hermit crab is looking for a new home. Hers is in serious disrepair.


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[Assistant] But finding the right fit isn't easy. She's only the size of a golf ball. But hermit crabs can grow as big as a coconut and must keep upgrading their shells to match.


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[Assistant] With a never-ending demand for bigger homes, hermit crabs have come up with an ingenious solution.


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[Assistant] When a crab finds a shell that doesn't fit, it signals to others to come and inspect.


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[Assistant] They form a housing chain. Big to small.


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[Assistant] Further inland, rivers are still raging from the last storm, carrying rainwater out to sea.


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[Assistant] Try Try Gobies. Travel upstream to find the best place to breed.


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[Assistant] He's smaller than a teaspoon. But using his fins as suction cups and his mouth to grip, he can conquer rapids.


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[Assistant] A Jamaican montane crab is only the size of a sugar cube.


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[Assistant] It doesn't need to be in water to survive. But a mother still needs it to breed.


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[Assistant] Using specialized hairs, she draws a film of water onto her shell.


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[Assistant] Perfect to hold a few drops. And secure enough for a new family.


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[Assistant] Twenty little crablets, each no bigger than a grain of sand.


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[Assistant] As storm season progresses, there's so much moisture in the air.


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[Assistant] This fearless father still has two more weeks of guard duty before his eggs finally hatch.


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[Assistant] Soon to be a mother again She needs to build a new nest.


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[Assistant] Spider webs make the ideal glue to stick it all together.


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[Assistant] And in just a few days, her nest will be ready to cradle a new clutch of precious eggs.


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[Assistant] As ocean temperatures rise through the year, they fuel ever more powerful tropical storms.


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[Assistant] Caribbean annals are surprisingly well adapted to endure such extreme weather.


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[Assistant] Extra large footpads help them hang on in winds of 200 kilometers per hour.


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[Assistant] But of the thousands of islands, only a few bear the full brunt.


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[Assistant] Tiny animals often have the best chance of sheltering from the storm and recovering quickly.


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[Assistant] In the middle of storm season come rain or shine, hermit crabs head back to the beach.


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[Assistant] Millions of microscopic larvae swept out to sea.


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[Assistant] Until it's time to come ashore and colonize new lands.


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[Assistant] Their father's devotion has given them the chance to make their own way in this big, wide world.


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[Assistant] A new generation of tiny miracles found nowhere else but on these treasured islands.


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[Assistant] Coming right up, boss! Uh, Bang? Bang!


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[Assistant] What is my wife doing back there? What's she doing?


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[Assistant] I've been with the same team for 10 years, but I feel like I've been with three different teams.


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[Assistant] I was a rookie coming into an unstable organization that...


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[Assistant] Really had no identity. We just played at an exciting brand of basketball. We had really loyal fans, but we didn't win.


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[Assistant] And I was just learning the ropes, trying to get better. And we slowly started making strides, but it was slow.


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[Assistant] We turn the corner, becoming a playoff type team, and...


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[Assistant] When you talk about being the man and all that type of stuff, that's when I had my experience in going through that. The spotlight and the attention and all that type of stuff. You know, just getting blown out the roof.


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[Assistant] And then the third team I played for is this current edition where we have Kay, Draymond, Clay, Andre, Sean.


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[Assistant] Our ceiling is just way up there and...


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[Assistant] There's sacrifice that comes with it and that's the part that took me a minute to adjust to and learn. When K made the decision to come.


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[Assistant] And play, you know, for us, the first thing we talked about was...


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[Assistant] Doesn't matter who's the face of the team or who's the face of the organization or who's the man. It matters how do we elevate each other's games out there on the floor? How do we, uhm, how do we help each other be great?


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[Assistant] Something special about that chemistry, on any given night, you don't know who's gonna give it to you, but.


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[Assistant] I was myself again, uhm, I would say a while is in like almost a year and a half.


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[Assistant] We were nine and one at one point. Everybody was, was clicking. And then we got some drama on the team and it becomes a different type of season, just like that.


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[Assistant] And the spirit is broken. It's tough to win a basketball game.


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[Assistant] It's self-inflicted or it's been dealt to us with injuries and all that type of stuff. Like, it's a process to get through it, but...


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[Assistant] Thankfully I learned team chemistry, how important that was, like in high school.


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[Assistant] You know, going to a Christian school and playing for a coach that


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[Assistant] Wanted to build our faith as well as our character as well as us as basketball players. There's a respect and care level in terms of valuing each person on the team and their story and their background and what they bring, you know, to the table.


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[Assistant] That's where I first started to understand the concept around, like, you can have as much talent as you want and you can be the man on your team or whatever, but in basketball you're only as great as the sum of the parts.


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[Assistant] I was an emotional kid. I cried when I got a B.


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[Assistant] We didn't win a state title or anything in high school, so it didn't really work out, but the experience was kinda what mattered in terms of setting me on my way.


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[Assistant] It's really easy for us to not work as hard on a daily with our individual routines or try to take shortcuts with not watching as much film or not talking about X's and O's as much as we're used to because you feel like you got it all figured out or


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[Assistant] When things go south, you have to let people be who they are because there's no changing that. Like Draymond, he's just loud and monstrative and when he's in a room, you're gonna know he's in the room. And...


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[Assistant] We need that. You know, we need that fire when we're out there in the court. Clay is kind of just in his own world all the time.


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[Assistant] And that's what makes him special because nothing really gets to him. And so he's just Mr. Consistent for us. And then K is, K is just a basketball savant. Everything he talks about, he's reading about, he's thinking about us about the game.


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[Assistant] He's so good at what he does and he always is trying to get better. It makes you uncomfortable to think like there's another level for this guy and he's trying to find it. Like we're all so completely different.


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[Assistant] We know we're human at our core and there's stuff that's going to bother us, whether it's justified or not. And...


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[Assistant] You know, rallying the troops and re-centering ourselves and refocusing ourselves on what we're doing, but the focus has to be on winning basketball games.


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[Assistant] It hits the fan throughout this course of the season. You have to bring a little bit more purposeful, positive energy. Really trying to get a smile out of somebody on the court. That's my type of leadership.


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[Assistant] What we have accomplished to date is historic, but the story is still going.


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[Assistant] So, I just don't want to take it for granted cause it is, it's special.


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[Assistant] Uh, you can bring him right over here, it's fine. Sorry.


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[Assistant] I'm Chris Brown right now. The door dash thinks I'm Chris Brown. So you can, who you wanna be? I won't, we'll carry it in.


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[Assistant] Uh, hello? Sorry. Lying on my side makes it hard to stay awake.


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


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[Assistant] Without him, I don't give the Warriors a chance.


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[Assistant] Well, what I've watched the Toronto Raptors do is make all of us, including myself, look like absolute positive idiots. Kawhi Leonard is simply on another level and everybody else is just spectating.


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[Assistant] He would see where a guy should cut to the basket, so he would throw that pass there while the guy didn't cut to the basket.


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[Assistant] It's a high IQ for the game, but he is such a great teammate. There are certain times we would have to tell him, we want you to shoot five times a quarter.


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[Assistant] At first, I think it's a little bit difficult for him because he really...


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[Assistant] So, he shouldered his responsibility and he got everyone else involved.


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[Assistant] He had a very good way of communicating to his teammates. Sometimes guys are aggravated or agitated and they're yelling at their teammates. It just doesn't come across as very encouraging and he had a very good way of, hey man, I want you to get the next one.


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[Assistant] Were you able to put into words the challenge of regrouping after something like that?


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[Assistant] We got the answer, boss! We know how eyes see!


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[Assistant] It's a confusing mindset to be in because I understand I'm at the height of my powers.


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[Assistant] My team, we won three out of the last four championships and playing in an All-Star game. There's nothing underdog about that at all. But.


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[Assistant] The underdog mentality and mindset will always be a part of who I am.


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[Assistant] Humble beginnings in the game, being overlooked and having to really on the court fight for everything. That is still a part of who I am and my DNA.


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[Assistant] I don't think you ever lose that no matter what the circumstances around you, how they change. And it's what makes me who I am. So, whether you just catch it out to the story or...


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[Assistant] Just tuning in or whatever the case is and you kind of feel like this is, I've always been on top, like that's not, that's not the case.


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[Assistant] As many times as you're an All-Star, you should never ever take it for granted. Like, injuries happen, team situations, like, sometimes the ball just don't go in the basket. Just being there is kind of like a good checkpoint in terms of being a dominant player in the league. It was my sixth time, and besides the very first one,


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[Assistant] Probably the most special weekend just in terms of being back in Charlotte.


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[Assistant] When I go places and I see parents, they'll go out of the way to come up to me and say, thank you for being a great role model for our kids.


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[Assistant] I don't feel like I'm doing anything uh, ordinary.


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[Assistant] Winning championships and all that, yeaah, that's all amazing stuff, but the way that I handle myself and like who I am as a human being and a person, I don't feel like I'm doing anything out of the ordinary, special, just being myself.


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[Assistant] It's confusing in my head sometimes, like I'm a role model slash inspiration.


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[Assistant] I feel a certain responsibility, but it's not like, I don't think it's hard. That makes sense. It's just me and myself.


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[Assistant] Learning about myself, about people around me.


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[Assistant] Or what it takes to be a true leader on and off the court.


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[Assistant] Continuing to grow and getting challenged and getting out of my comfort zone.


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[Assistant] A lot of my family's still based in Charlotte, so that's like the hub.


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[Assistant] Uhm, kinda spaced out around the country a little bit and so you can't get everybody in the same room that often anymore and those moments are special.


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[Assistant] This is the Davidson locker room right now. They just dressed it up. Uh, All-Star weekend in Charlotte, obviously we back. I get to go to the game. They play St. Joe's tomorrow night. And so it's part of All-Star weekend. Got to lace the team with, you know, the All-Star kicks, but they did it to a whole nother level.


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[Assistant] I met Bryant my senior year after I committed to Davidson. We knew we were going to be roommates, but I didn't really know much about him.


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[Assistant] Except that he was from Maine and I had never met anybody from Maine before.


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[Assistant] Music, he was playing in the dorm. There's a lot of fights over that.


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[Assistant] Growing up in Charlotte, you thrown on TV either watching the Hornets or ACC basketball.


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[Assistant] Honestly, I was a Duke fan growing up and envisioned myself, not that it wasn't the only choice, but I knew I wanted to play in the ACC. Recruiting started in sophomore year.


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[Assistant] I went to a couple of summer camps, got in front of a bunch of recruits and coaches and.


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[Assistant] That look, like, that comment's like, alright, this is my time.


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[Assistant] It's my junior year, couple feelers from Virginia Tech. Played in front of Roy Williams, Coach K in Vegas. Didn't get a call from them.


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[Assistant] And that's where it kind of sunk in like, alright, like what's going on?


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[Assistant] He's got a high IQ. He can shoot, but he's undersized. He can't defend or score on guards that are bigger than him in the ACC.


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[Assistant] Gonna fast forward to my senior year where Virginia Commonwealth, Winthrop University, and Davidson came to a couple workouts and all three of them offered. That's when I kinda started to think like, maybe my journey's gonna be a little different. Eventually just came down to going somewhere where


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[Assistant] Coach McHale really looked me in the eye and said, I can make you the best basketball player you're going to be.


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[Assistant] It won't be ACC, but I can go somewhere where the coach and the program really values what I can bring. I'll get a chance to play right away.


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[Assistant] And make some noise and see what happens. And so I get to school, like everybody's like noticing like a little sly, you know, smile on my face. Like, what's up with you? He's like, I just committed to Davidson.


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[Assistant] And they popped the wind right out of my sails like, where? Like who is that? Where are you going?


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[Assistant] The NBA was, it was a goal, but that wasn't a power move.


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[Assistant] Position myself to get to the NBA because if you look at their track record, I want to say Fred Hetzel and...


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[Assistant] John Gertie and maybe one other guy actually made it to the NBA. Definitely a leap of faith to them.


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[Assistant] Just fully commit to it and make the most of the opportunity.


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[Assistant] We were playing Gonzaga. It was a different feeling because you understand the magnitude of playing in NCAA tournaments.


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[Assistant] I made it, and my first thing was appointing my parents.


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[Assistant] The fact that I had to go to Davidson in the beginning to confess my parents gave me that that was the right decision.


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[Assistant] Playing under my dad's shadow in Charlotte. All that type of stuff just came out in that moment. And...


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[Assistant] Definitely understood the magnitude of that win for everybody.


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[Assistant] Including Coach McKillip in our school who hadn't won an NCAA tournament game in almost 40 years at the time. So definitely understood how important and special it was.


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[Assistant] It has such a dramatic effect on how the game is viewed, played, how teams are put together, all that type of stuff.


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[Assistant] Especially being overlooked and critiqued and having to really fight for everything that I was able to get. I don't know if I'll understand the true magnitude of it until I get done playing, but like even right now I'm aware.


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[Assistant] Welcome to this video on learning Elasticsearch 5.0, entitled Running Log Analysis.


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[Assistant] In this video, we kick things off by preparing for log analysis. Basically, we verify the files, configuration settings, et cetera. We then test our pipeline right before running the actual analysis that stores data in Elasticsearch. Finally, we create an index pattern in Kibana and view our data. So let's get to it.


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[Assistant] Okay, the first thing we're going to do is we're going to look at three key files, two of which are configuration files.


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[Assistant] Okay, so first, let's look at the filebeat.yml file. As per the previous video, as you can see here, we have our


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[Assistant] Input type, which is log. We have an absolute pad to our logs dash dot log file, which is our data file.


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[Assistant] And then down here, the relevant thing is the output.logstash information. You see we have the host and it's for localhost.


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[Assistant] Then we have the filter as mentioned in the previous video, we're using grok, which is going to parse the actual unstructured message and make it structured from the Apache log files. And then there's geo IP and we get the IP address from the client IP field. Finally, there's the output here. We're going to test first.


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[Assistant] And that's why we're using the codex with the value Ruby debug. You have here a standard Apache log file. So you can see we have the IP address, timestamp, et cetera.


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[Assistant] Okay, the first thing we need to do is start a file beat. So in doing that, we're going to need to map to the file beat directory.


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[Assistant] So do command and there you go. And here you're going to see errors, but don't worry about those. They come up because we haven't started logstash. So now let's open another window.


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[Assistant] We have positive validation. You see config validation result. Okay.


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[Assistant] Now we want to actually run our pipeline, but before we do that we have to do a couple of things.


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[Assistant] The first thing we need to do is update our Logstash configuration file.


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[Assistant] Elasticsearch where we list our host. Our host obviously is on port 9200, which is where the Elasticsearch cluster resides. We save this.


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[Assistant] And now we have to come back to file beats and do something quickly. We need to first of all, stop it and we need to delete.


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[Assistant] The registry file because we're going to run across the same data.


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[Assistant] But this time we're going to start LogStash with the remote automatic option, which


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[Assistant] It's important because it allows you to update the Logstash configuration file without having to stop and then restart Logstash. So let's do that.


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[Assistant] Okay, and this is a good sign. Okay, now that we've actually successfully piped our logs to


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[Assistant] The Elasticsearch cluster. Let's look at them in Kibana. But first what we need to do is create an index pattern. So to do that, if you guys can recall, we go to management, we select index patterns, add new, and here's our either for logstash. And the wildcard again, because logstash


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[Assistant] As you can see, we have all of our log data here in short.


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[Assistant] And I'll leave you guys to play with this. There are many different permutations. And I'm going to close out by sharing.


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[Assistant] So, with Apache log files, there are two commonly used formats. One is referred to as common log format, and this is the one that's in our sample file. The other is called the combined log format. If you notice here, it's just like the common log format, except


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[Assistant] There's an extra part to it, and I've highlighted that in orange.


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[Assistant] When you're parsing using the combined Apache log parser with grok, the problem is you may encounter an issue with parsing combined log format files. At least I have on several occasions.


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[Assistant] How do you verify this? Well, you verify this by first


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[Assistant] Going into Kibana after you've run your pipeline, go into Kibana and then click on DevTools. Run the command.


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[Assistant] To check out the mapping, so you want to look at the logs dash index and that's going to probably be logs dash dash the current date.


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[Assistant] If you're using the default settings for Logstash, forward slash underscore mapping to call the mapping API. Look for fields like the client IP field. If it doesn't appear in your mapping.


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[Assistant] Then most likely it didn't parse correctly. So how do you handle that? Remember originally we used to combine Apache log pattern. Well, instead replace this with the following regular expression pattern.


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[Assistant] So if you're having difficulties parsing your Apache log file, because it's the combined log type, no worries. Just try this pattern. It should allow you to parse. That's just an FYI and it's based on my own personal experience. And that concludes this section.


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[Assistant] Accordingly, we covered the end-to-end process of weblog analysis. More specifically, we covered the process of configuring filebeat and logstash.


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[Assistant] We then tested our pipeline and ran an analysis and then viewed the results in Kibana.


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[Assistant] However, this demonstration was more than sufficient to get you well on your way to building an effective log analysis system. Next up we look at advanced queries in Elasticsearch.


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[Assistant] I think what George is trying to say is that we're on planet Earth. Uh huh. What?


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[Assistant] Maybe you need to squint really hard. Ugh.


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[Assistant] Hello again and welcome to the second video. In this video tutorial we are going to take a look at


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[Assistant] More about FireMarket Framework, 2D industry development basics, and we will learn how to use effects in our applications, and we have demo applications for learning proposals. So...


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[Assistant] The FireMonkey, the first native CPU and GPU powered application platform for building rich business applications. FireMonkey includes a full suite of drag and drop user interface controls, and you can use buttons, tags, combo boxes, tables, tables, panels, and more to design your user interface.


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[Assistant] What I make is solid controls such as STPanel, Energy Toolbar, 3D Controls,


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[Assistant] Non-streamable classes, these are part of the RTL, and streamable classes are part of the RTL. RTL means Runtime Library.


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[Assistant] MateFX supports multiple filters simultaneously leveraging a single dynamic GPU instruction pipeline for its near real-time pixel perfect performance. Now it's time to demo.


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[Assistant] I choose the Hydra Footwear application type, click the OK button. And we have to save our project. And it will generate some


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[Assistant] And also we can use other effect components.


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[Assistant] So, this application is installing to my device. So, this is hdc application demo. We can run it. And click me. No, no button click it. Yes, yes button click it.


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[Assistant] There's also veins that are kinda bone, I think. 34, 35, 36, 37, 38. 38.


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[Assistant] Sydney, do you know what happens to the food you eat after you chew and swallow? Yeah, but what else happens?


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[Assistant] So, we have begun by talking about channel equilibrium and


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[Assistant] Uh, this was the background to the course so that you're all familiar, those of you who haven't taken freshman economics or intermediate economics, what's the background that's required for the course? And the, the fact that general equilibrium is a part of finance is


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[Assistant] As I say, a bit of an innovation because most finance courses are taught entirely independently of economic theory, but the two greatest Yale economists and two greatest Yale financial economists


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[Assistant] Take up the question today of why is the free market supposedly so good? So we worked out two examples last time. The first example


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[Assistant] And the two examples are very similar to the first problem set that you did.


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[Assistant] The heart of perfect competition in the economy is that there's lots of people, and so, we can't, yeaah, I think, alright, from now, from now on, I'm going to be at the end of class. So, from, so,


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[Assistant] So we mean a million people of type A and a million people of type B. So the utility function of A, the welfare function of A is written there, and the welfare function of B is written, and they each begin with endowments.


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[Assistant] Uh, so when these two million people come together, they're gonna be doing the same thing that you saw in the class on the first day. They're gonna be trying, they're gonna be haggling and arguing and the


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[Assistant] The buyers are gonna always say the stuff's not worth very much and why should they pay so much and the sellers are gonna say it's worth a tremendous amount and they should pay even more and eventually they're gonna come to prices.


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[Assistant] And we discovered last time that the way of describing what prices and what final allocations they come to is by writing down a system of equations. And you did that all in your homework, and we came up with this outcome.


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[Assistant] So similarly, we did another problem in the last class, and we wrote down


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[Assistant] Uh, you know, different welfare functions or utility functions and different endowments and again we got the equilibrium. So we said that one of the amazing things is that these system of equations, the six equations for each economy


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[Assistant] That you solved in class always have a solution. So the people who discovered that


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[Assistant] I discovered it at the Cole Foundation. They were Ken Arrow, uhm,


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[Assistant] Gerard de Bru, who is a Yale assistant professor. Ken Arrow, who is visiting the Coles Foundation in Chicago, but was a Stanford professor.


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[Assistant] And, and, uh, Lionel McKenzie, who actually taught at Rochester, the economic equations always have a solution.


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[Assistant] So what's so good about that economic solution? Adam Smith talked about the invisible hand, but you know there was no mathematics in Adam Smith. So why do we think


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[Assistant] Equilibrium is such a good thing. After all, you saw in the example that there were three pairs of people who forlornly couldn't trade at all. So, clearly, not everybody gets to trade. Discrimination happens. Some people get the stuff, other people don't get the stuff. Well, the first approach of economists, uh, was that


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[Assistant] Equilibrium maximizes the sum of utility.


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[Assistant] In this example. So this example is very special because everybody thinks that the good y has constant margin utility of 1. So that's going to play a big role in our optimization.


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[Assistant] In maximizing the sum of utilities. So if you want to maximize the sum of utilities, you want to maximize the sum of utilities, you have to maximize, uhm, 100 XA,


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[Assistant] Minus one half xa squared plus thirty xd minus one half xd squared, uh, squared.


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[Assistant] Let's put at the end, yA plus yB.


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[Assistant] So the claim is that if all these 2 million people meet in their


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[Assistant] Meet in a room like this and start shouting at each other and trading and offering deals and stuff. It's gonna come down to a final allocation which we've written over there where A gets 77 of the first good and B gets 7 of the first good and that final allocation is gonna maximize the sum of utilities.


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[Assistant] So why should that be the case? In fact, in this example, it is the case. Well, why is it the case? Because there's constant margin utility of y.


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[Assistant] We don't have to worry about the Y allocation at all.


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[Assistant] Because if you increase yA and decrease yB, they still have to add up to 6000, you're going to be lowering


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[Assistant] A's utility and raising B's utility by the same amount so you won't have changed the sum of utility.


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[Assistant] So the y's are totally irrelevant to this maximizing the sum of utilities.


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[Assistant] So all we have to do is make sure that the x allocation maximizes the sum of utilities for the good x. So that, you can see, must be the case because


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[Assistant] We can, we have to maximize the sum of two people's utilities, or two million utilities. You can always plug in x


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[Assistant] D is a function of xA, namely 84, let's write it as a function of xA.


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[Assistant] Okay, and we can replace this maximization by replace, by, by recognizing that xB is just a function of xA.


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[Assistant] Okay, in fact, it's a very special function where the derivative of xB


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[Assistant] Taking into account that xB is just a function of xA because if we, you can't, to maximize you have to keep feasible. So we've now ignored all the constraints. Constraints have now


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[Assistant] The equilibrium 77 and 7 maximizes this.


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[Assistant] Well, the key is diminishing margin utility. We know that the utility function for, uh,


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[Assistant] Utility function for A, you know, is quadratic, so it looks something like that.


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[Assistant] Okay, and the utility function for B also looks something like that. And so, if you look at this


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[Assistant] Function, entirely as a function of A, is also going to look like that. Okay, so by diminishing margin utility,


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[Assistant] Concave. Concave, remember, is a picture like that. So what is the essence of a picture like that? It means that setting derivative equal to zero


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[Assistant] Equal to zero is equivalent to implies you, you are at max.


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[Assistant] You know, if you have a function like that and then it goes up like that, you could have the derivative of zero here and not really be at the max. But because of diminishing marginal utility, more and more of x


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[Assistant] At xA equals 77 has to be the case because if you plug in xA equals 77 and you take the derivative of all this with respect to xA


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[Assistant] Why is that? Because you know the chain rule. If I differentiate everything with respect to xA, and xB is the function of xA, it's the


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[Assistant] The derivative with respect to xB is xB times the derivative of xB with respect to xA. So it's that. But if you plug in xA equals 77 and xB equals 7, dxA


[User] Please transcribe this text

[Assistant] dx v dx a is minus 1, you just get 23 minus 23 equals 0.


[User] Please transcribe this text

[Assistant] Because the marginal utility of A is equal to the marginal utility of B at equilibrium, the sum of utilities is maximized. And that's the end of the story.


[User] Please transcribe this text

[Assistant] You noticed in the problem set, something like that. You may not have given exactly this argument, that was what you were supposed to have sort of discovered in doing the problem set, and now you've confirmed what you already pretty much knew. So, are there any questions about that?


[User] Please transcribe this text

[Assistant] Okay, it's just the simple generalization of our football ticket example where the football tickets end up in the hands of the people who want the more, the most.


[User] Please transcribe this text

[Assistant] And A happens to like a lot of tickets, not just one. And so A is going to keep buying tickets until


[User] Please transcribe this text

[Assistant] He's bought 77 of them. And by then, the next football ticket's worth less than 23 to him. B, she likes football tickets a little less. She's not gonna buy as many. By the time she gets down to, uh, seven football tickets, she's gonna think,


[User] Please transcribe this text

[Assistant] Uhm, so she hasn't bought nearly as many. When she's gotten to seven football tickets, she's gonna think the next one's worth less than 23. And since the price is 23...