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Transcript: Welcome to this neural network programming series. In this episode, we will dissect the difference between concatenate and stacking tensors. We'll look at three examples, one with PI torch one with tensor flow and one with numpy. Hey, by the way, do you...

Transcript: Hi, welcome to Jay's video. This video is going to be about my master's degree and machine learning. I'm not done with a whole degree yet. I have a little bit less than a year left, but I'm done with all my mandatory courses and also almost all my...

Transcript: Hey, there, how's it going? Everybody in this video we're going to be looking over time series data and plotting dates in Matplotlib. Now there's a ton of data out there that contains date information so knowing how to plot this properly is definitely...

Transcript: Let us look at an example of forecasting using the time series methods. The past data for the demand of a product is given to us in this table so for the month of June 2013 the demand was 584 units for Feb. 2013 The demand was 610 units for March 2013 The...

Transcript: Hey, guys, welcome back in this video. We'll be talking about a pretty interesting topic. In time. Series analysis called anomaly detection. So as you probably know, an anomaly is something that's out of the norm or something that is unexpected and with...

Transcript: [music] All right, everyone! I am here with my friend. Robert Osazuwa nest and we're going to spend a few minutes chatting about the interview and how it relates to his causal modeling and machine. Learning course, Robert. Welcome back! Hey, Sam, thanks...

Transcript: Hey, guys, welcome back to this class. Unsupervised machine learning hidden. Markov models in Python. In this lecture, we are going to go over the very important scan, function and piano. Why is this important think about how? Fianna works! You have to...

Transcript: A very common operation that we'll come across with deep learning is convolution. We're going to explore what this means using our new. Gaussian kernel that we've just created for now just think of it. As a way of filtering information, we're going to...

Transcript: What is the best loss function? The age-old question in machine learning? Will we solve this problem today? Nope, but we will talk about some loss functions, their pros and cons and even discuss a recent paper on adaptive loss functions. So that would mean...

Transcript: Hello, people from the future. Welcome to harmonized nerd in this video. I'm gonna be talking about two very important concepts in NLP that are text, summarization and keyword extraction well. I will be doing these things. Using a famous library called...

Transcript: So, hi, everyone and welcome to this video. The second video in our series for univariate forecasting in our and in this video we're gonna test for non stationarity, so we're gonna conduct three tests for a non stationarity, so we know that to be able to...

Transcript: Hello, everyone and welcome to this video. On Keras, versus tensorflow versus pie Torch! So what can you expect from this video? What's in it for you first? We will introduce you to each of these platforms. Tensorflow Keras and Pytorch In Brief, Then we...

Transcript: What is going on everybody and welcome to part two of the tensor flow objects detection. API tutorial in this video we're going to be showing you guys is how we can do something a little more custom than just loading in simple images. We can also load in...

Transcript: Hello, everybody And welcome to the part 2. Of the Tensorflow Serving example. In the first part, we prepared and labeled a training data set of the model, which should predict the probability of the profit. In the second part, we will create our model...

Transcript: Oh, hello, world. This is such a and in today's video. We're going to be looking at the basics of translation, so let's get started in order to be doing translation. We're going to be looking at this particular thing called sequences sequence and...

Transcript: Hello, explorers and welcome to another video today. We're gonna talk about evaluation, so we're gonna evaluate what we have done so far. How good are we? How close to the truth, are we? What is the actual status of our trained model and so to evaluate our...

Transcript: Hi, welcome back Programmers and the wild episode of Tensorflowjs Teknik. Because in the previous Dimention Optimizer determine the lost and prediction price sold the description of the seasons and wedding placeholder and growing the stock exchange so much...

Transcript: Hello, I’'m Robert Crowe. Today I will talk about Tensorflow Extended. It is also called TFX and how it can help you. Put the magical machine learning model into production. This video is real-world machine learning. The first episode of the Five-part...

Transcript: Hello everyone My name is Alex Today I want to introduce Eager Execution to everyone You should have heard this name in the first two lectures. But I will give you a better introduction This is necessary and object-oriented Pythonic new way to use...

Transcript: Neural networks learn by calculating the difference between their current output and the true values. It's learning from this difference is encapsulated in a loss function. The process of training and neural network involves minimizing this loss function...

Transcript: What's going on everybody? And welcome to part 4 of the deep learning with Python Tensorflow and KAOS tutorial series in this video and the next video, what we're doing is talking about how we can analyze and optimize our models using Tensor Board. Now,...

Transcript: Let's say that! I wanted to specify the temperature in a city like New York. For instance. How would I do that well? I could simply use a number with a corresponding unit like 273 Kelvin, which is equivalent to zero degree Celsius and 32 degrees...

Transcript: Welcome back to this series on no network programming with Pi Torch in this video, we will kick off Section 2 of the series, which is all about tensors. We'll talk tensor's terminology and look at tensor indices. This will give us the knowledge we need to...

Transcript: So, in this video, I want to talk about inner and outer products. It's a good time to introduce these, because now we know about the transpose operator, so let's start with the inner product. This is the inner product between two vectors, so we have two...

Transcript: Hi, welcome you again. And in selected. I am going to show you our Fiji about. Angie tents or identity tensor and the cancer in verse, right, so the two gotta pixel here in this letter. Number one identity tensor. I then beauty cancer and inverse stencil...

Transcript: Hello, this is a bit again, so I'm going to discuss today. All about the eigenvalues and eigenvectors of attempts of oh here is slowed down again. I can values and eigenvector's. So what is eigenvalues? And what is eigenvectors of the tangent, so say, for...

Transcript: Hi, everyone, my name is Manuel Parente. And today I'm going to present our in-text speech paper about asteroid. The pythog-based audio source separation toolkit for researchers. I first explained motivations behind the creation of asteroid its features...

Transcript: Hi, so before. I get into the main content in this video, which will be tabular data. I wanted to mention these videos here. Intro to processing for database They are made by Mathew Epler. I will include a link below. I recommend you watch these videos...

Transcript: Hi, everybody, this is. Eugene Ole our connection computing at the National College of Ireland and welcome to my series of short how-to videos in this video. We want to learn how to perform a paired or dependent t-test, and we're going to learn how to do...

Transcript: Welcome to stats cast. The purpose of this series is to explain statistical concepts in a way that is clear and simple, even if you don't have any previous experience with stats. This video explains the purpose of t-tests how they work and when to use them...

Transcript: Okay, so in this video. I'm going to take a look at the students t-test and the T distribution, which is a continuous probability distribution that arises in estimating the mean of a normally distributed population when the sample size is small and the...

Transcript: What we're going to be going over here is linear regression and we're going to be testing for the significance of the independent variable here and really what we're doing here is we're trying to develop and determine if there is a relationship here...

Transcript: Hey, guys, you're watching. Python tutorial videos on my Youtube channel Python from across campus in today's tutorial, let's look at how performing image segmentation, using support vector machines or SVM for short in the last few tutorials. In fact, in...

Transcript: Of basically city streets, right and some of the streets have houses and some of those houses have house numbers, and so the authors of the data set came up with a way to like, grab those numbers out of the images and then basically label them and lets....

Transcript: Hello, everyone will come back again. My name is Jesse. And in this one up tutorial, we learn about how to do test summarization using an adapter kit called Sue right or Sonny, So Sue me is a very powerful Python package for summarization. Then it gives...

Transcript: Yes, first of all, some have always wondered where youth comes from. Doesn't come from a student, but from the good gentleman who was called. The one for the tests are responsible for the development. You wanted somehow not that his name will be published,...

Transcript: The t-test was developed by William Sealy Gosset he worked at the Guinness Brewery over a hundred years ago and he developed this test to determine things like the difference between barley yield. Now he wanted to publish this statistical test to share...

Transcript: How's it going guys today? We're going to be going over another interview. Question together today. Our question again is on leak code as always, our questions from Amazon and it's called string progression. All right, guys, before we start our video...

Transcript: [MUSIC] Hey, come on, take me is touching touch between me and that since you’'re just wanting to celebrate the silly sandwich, start this look, look. This is not the time after [Music] bang still won't even detail. Mama had a day. April 1st, neither...

Transcript: In this video, we're gonna talk about how to calculate the standard error of the mean so. I'm going to give you two examples, so you could see some different ways on how to do it. Let's start with this one number. One five students in a college were...