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Transcript: [MUSIC] Okay, so in this video? I want to take a look at the new windows on Raspberry Pi build 0.3.0 and this is the latest version. It's just been released today and this version you have to build by yourself. You have to get your own whim, and then you...

Transcript: These are three, sloppily written and provided at a very low resolution of 28 x 28 pixels But your mind has no problem recognizing it as three and I want you to take a moment to appreciate How your brain can do this effortlessly and smoothly I mean this...

Transcript: Hi, this is Carrie Ann, and welcome to the Computer Science course! As we touched upon several times in this series, computers are miraculous at storing, organizing and Fetch and process huge amounts of data. This is ideal for things like e-commerce sites...

Transcript: [music] yeah. I know soon so as soon as we hit record, then the noises start so now a little landscaping, a little little construction. Uh, have you had it through the pandemic? Has it been hard kind of doing stuff like this? Yeah, weirdly, it's gotten...

Transcript: Now that we know about limitization, it's time to use limitations present in an ltk package, In particular, we will be using word limitizer so wordnet is a collection of valves adjectives, nouns, adverbs, and these are grouped together on synonyms of these...

Transcript: Narrator> Word Transformers [ footsteps ] [ engine sound ] [ electric motor sound ] [ click ] Grandpa> ha ha I'm in the letter C...hey Timmy wait up you're going to fast ha ha Narrator> C [ engine sound ] [ engine sound ] Narrator> C [ engine...

Transcript: [MUSIC] This video will explain the wide residual networks otherwise known as wide ResNet and then frequently abbreviated in papers as wrn - the number of layers - the widening factor, so the headline from this paper is that a simple 16 layer wide. Resnet...

Transcript: StatQuest Check it out talking about Machine-learning. Yeah StatQuest Check it out Talking about cross-validation StatQuest Hello, I'm Josh stormer and welcome to StatQuest today we're going to talk about cross validation and it's gonna be clearly...

Transcript: You have probably heard scientists quote p-values whenever they report the results from their experiment. But what exactly is a p-value anyway, in this video? I would clearly explain what a p-value is. A p-value is an abbreviation for probability value and...

Transcript: Hello, all my name is. Krishna and welcome to my Youtube channel. Today, we will be basically discussing about cosine similarity and cosine distance now in my previous video. I have already explained you about Euclidean distance and Manhattan distance. I...

Transcript: Alright, so, in this video, we'll be going over the binary search algorithm in Python, we'll be covering both an iterative and recursive version of binary search. And if you're not familiar with what binary search is basically, it allows you to very...

Transcript: Hello, everyone, let's quickly. Go over! What is loaded and softmax? If you are working on the tip loading model for the multi-clas's classification, you might heard about the softmax and the logit and here is the example we have the input. Which is this...

Transcript: Hello, everybody! I wanted to give an introduction to the wasserstein distance. The vasserstein distance is used in applied topology for a variety of things distances between persistence diagrams or to define metric, some pressure thickenings. But today I...

Transcript: We're gonna work through some plots in Python, So come to the course website. Come to AP minor comm Ch E 263 and the one that we're gonna be working on is under the homework and then. If you go to homework 9 you're gonna see, you know, this is the printout...

Transcript: [music] hello! All my name is Krishnak and welcome to my vlogging channel. So guys, we'll continue the discussion with respect to advanced convolution neural network architecture, and in this particular video, we are going to discuss about the VGD16...

Transcript: [MUSIC] In this video we're going to walk through how to code the VG architecture in Pi Torch, but let us start with understanding how it works first. So here's the VG paper. We're not gonna read entire paper. We're just gonna look at the the part where...

Transcript: Okay, so this is the introduction lesson on the scaler or dot product for index notation. What we're going to do is going to break down thing into all its components and show what it ends up equaling. And why it equals that, okay. So what we want to do...

Transcript: When raising a gambler from English people look at you care for a vector Christmas wishes. She is very convenient for wealth and ethical behavior of once-in-life. Google search, either in space and time date printing and first time, is over time in the...

Transcript: [MUSIC] Hey, guy's in this video! We're gonna do a really quick introduction to vector. Auto regression. So this is a request. I had to do this video in a couple of the comments in previous time series videos, it turns out vector autoregression. The idea...

Transcript: One in this video we're going to have an example of conducting the vector autoregression using art in particular, we're going to be using Philippine data and we're gonna try and explore associations between unemployment and GDP, and these associations...

Transcript: Well up! Sampling is the process of increasing the sampling rate, Of course, the signal has already been sampled, so it's in discrete time and we're going to take those samples and we're going to do a discrete-time interpolation to construct a...

Transcript: When we come to the data section here that we've seen in previous videos and I need to say, I need a temporary piece. It's a useful piece piece of Ram. I will say memory and give it a data type. D word, which means 4 bytes and lets initialize it to 0 This...

Transcript: Welcome to our lesson on scatter plots. A scatter plot is a graph of plotted points that shows the relationship between two sets of data. There are many types of relationships that could be distributed into a scatter plot. You could have a positive linear...

Transcript: Hi, and welcome to an illustrated guide to recurrent neural networks, A Michael, also known as learned Vector. I'm a machine learning engineering, the natural language processing and voice assistance space. If you're just getting started in machine...

Transcript: When the equation seemed too complicated, stat quest to the rescue, Hello and welcome to Star Quest Stat Quest is brought to you by the friendly folks in the genetics department at the University of North Carolina at Chapel Hill. Today we're going to talk...

Transcript: Hi, guys, this is a Jonathan Lambert. With the mathematics development and support service at the National College of Ireland and this short video is going to detail how to undertake a single sample t-test and in particular, a two-tailed version of the...

Transcript: Hey, fingster's now! If you have come across this video, then it's quite possible that you have encountered the following error list. Indices must be integers or slices, Not string. Now, let's go ahead and understand the reason behind the occurrence of...

Transcript: To get a good encoding for the face image, One of the ways to train the variables of a neural network is Is to define and apply gradient descent to the triplet loss function Lets. Take a look at its meaning. To apply triplet loss, we need to compare image...

Transcript: In this video, we're gonna talk about Transformers, So we're not talking about Optimus Prime or anything like that, But an electrical device that's commonly found in an AC plug or an adapter and things like that. So what's the purpose of a transformer? A...

Transcript: Hi, it's a bit again, so in this lecture. I'm going to teach you about the test of the cancer. But Mr. Potential is one of the important properties of the tensor that is used in many engineering and do physical Geodynamic application. Especially in many...

Transcript: Hi, everybody! Welcome back to a new. Pi Torch Tutorial. This time. We want to talk about transforms for our data set. In the last tutorial, we use the Built-in data set and data loader classes, And if we use a built-in data set like we see here and we see...

Transcript: Welcome back to this series on neural network programming with Pi George. In this video, we will see how to work with a data set and data loader of Pi torch classes. Our goal in this video is to get comfortable working with these classes as well as to get...

Transcript: [MUSIC] What's going on? Guys hope you're doing awesome and welcome back for another pie tricks video. So I'm guessing most of you already know what Torch X is about, so most of you probably searched and clicked on the video, but for those of you who don't...

Transcript: Hello divers! My name is Ben, I am a technical diver, a cave diver, and a trainer. In this video, I give you 10 reasons to finally switch to a portable primary light like New Halcyon EXP, but also 3 reasons not to switch at the end of this video. So be...

Transcript: Hi, everybody! Welcome back to a new. Pi Torch Tutorial This time. We talked about the Softmax function and the cross entropy loss. These are one of the most common functions used in neural networks. So you should know how they work now. I will teach you...

Transcript: What's gone, guys in this video? We are going to be experimenting with imagenet models in hi torch. So in the last video, we went over how to load data and a little bit about pre-processing and now in this video, what we're going to do is we're going to...

Transcript: In this video, we will demystify the heavily underused notation einsum. [MUSIC] right. So what is Einsum? Well, it's an extremely general way of performing various tensor or ND array operations as you will see soon, but before we try to understand how it...

Transcript: Dropout was a strange technique for randomly deleting units in a neural network. Why does this work well with normalization? Let's find out why. In the previous video, we talked about the intuition of randomly deleting units in a neural network. So every...

Transcript: Traditionally, point products or something like that were introduced really early on in linear algebra Course Usually in the beginning. So it might seem odd to push them back that far in the series. I did this because there is a standard way to display...

Transcript: In this lecture, we are going to talk about the CNN, which is one of the most popular during the architectures, and this CNN works pretty well for image processing, NLP and so on, this is a typical scene in architecture, which gets images as an input. And...