Neural Networks / Deep Learning

1.

0:59

Happy Halloween (Neural Networks Are Not Scary)

My hope is that even though Neural Networks are not scary, people will still dress up in Neural Network costumes today. BAM!!!
2.

18:54

The Essential Main Ideas of Neural Networks

Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly understood. Everyone says Neural Networks are "black boxes", but that's not true at all. In this video I break each piece down and show how it works, step-by-step, using simple mathematics that is still true to the algorithm. By the end of this video you will have a deep understanding of what Neural Networks do.
3.

18:24

The Chain Rule

The Chain Rule is a method for finding complex derivatives and is used all the time in Statistics and Machine Learning. This video breaks it down into its two simple pieces and shows you how they easily come together. We then use the Chain Rule to solve a common Machine Learning problem - optimizing the Residual Squared Loss Function.
4.

23:54

Gradient Descent, Step-by-Step

Gradient Descent is the workhorse behind most of Machine Learning. When you fit a machine learning method to a training dataset, you're probably using Gradient Descent. It can optimize parameters in a wide variety of settings. Since it's so fundamental to Machine Learning, I decided to make a "step-by-step" video that shows you exactly how it works.
5.

17:34

Neural Networks Pt. 2: Backpropagation Main Ideas

Backpropagation is the method we use to optimize parameters in a Neural Network. The ideas behind backpropagation are quite simple, but there are tons of details. This StatQuest focuses on explaining the main ideas in a way that is easy to understand.
6.

18:32

Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.

The main ideas behind Backpropagation are super simple, but there are tons of details when it comes time to implementing it. This video shows how to optimize three parameters in a Neural Network simultaneously and introduces some Fancy Notation.
7.

13:09

Backpropagation Details Pt. 2: Going bonkers with The Chain Rule

This StatQuest picks up right here Part 1 left off, and this time we're going to go totally bonkers with The Chain Rule and optimize every single parameter in this simple Neural Network. BAM!!!
8.

8:58

Neural Networks Pt. 3: ReLU In Action!!!

The ReLU activation function is one of the most popular activation functions for Deep Learning and Convolutional Neural Networks. However, the function itself is deceptively simple. This StatQuest walks you through an example, step-by-step, that uses the ReLU activation function so you can see exactly what it does and how it works.
9.

13:50

Neural Networks Pt. 4: Multiple Inputs and Outputs

So far, this series has explained how very simple Neural Networks, with only 1 input and 1 output, function. This video shows how these exact same concepts generalize to multiple inputs and outputs and provides a context within we can discuss SoftMax and ArgMax for modifying the output data.
10.

14:03

Neural Networks Part 5: ArgMax and SoftMax

When your Neural Network has more than one output, then it is very common to train with SoftMax and, once trained, swap SoftMax out for ArgMax. This video give you all the details on these two methods so that you'll know when and why to use ArgMax or SoftMax.
11.

7:13

The SoftMax Derivative, Step-by-Step!!!

Here's step-by-step guide that shows you how to take the derivatives of the SoftMax function, as used as a final output layer in a Neural Networks.
12.

9:31

Neural Networks Part 6: Cross Entropy

When a Neural Network is used for classification, we usually evaluate how well it fits the data with Cross Entropy. This StatQuest gives you and overview of how to calculate Cross Entropy and Total Cross Entropy.
13.

22:08

Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation

Here is a step-by-step guide that shows you how to take the derivative of the Cross Entropy function for Neural Networks and then shows you how to use that derivative for Backpropagation.
14.

15:24

Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)

One of the coolest things that Neural Networks can do is classify images, and this is often done with a type of Neural Network called a Convolutional Neural Network (or CNN for short). In this StatQuest, we walk through how Convolutional Neural Networks work, one step at a time, and highlight the main ideas behind filters and pooling.
15.

16:37

Recurrent Neural Networks (RNNs), Clearly Explained!!!

When you don't always have the same amount of data, like when translating different sentences from one language to another, or making stock market predictions from different companies, Recurrent Neural Networks come to the rescue. In this StatQuest, we'll show you how Recurrent Neural Networks work, one step at a time, and then we'll show you their critical flaw that will lead us to understanding Long Short-Term Memory Networks.
16.

20:45

Long Short-Term Memory (LSTM), Clearly Explained

Basic recurrent neural networks are great, because they can handle different amounts of sequential data, but even relatively small sequences of data can make them difficult to train. This is where Long Short-Term Memory (LSTM) saves the day. Long Short-Term Memory is a type of recurrent neural network that can handle much larger sequences of data without those pesky exploding/vanishing gradient problems that plague basic recurrent neural networks.
17.

16:12

Word Embedding and Word2Vec, Clearly Explained!!!

Words are great, but if we want to use them as input to a neural network, we have to convert them to numbers. One of the most popular methods for assigning numbers to words is to use a Neural Network to create Word Embeddings. In this StatQuest, we go through the steps required to create Word Embeddings, and show how we can visualize and validate them. We then talk about one of the most popular Word Embedding tools, word2vec. BAM!!!
18.

16:50

Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!

In this video, we introduce the basics of how Neural Networks translate one language, like English, to another, like Spanish. The ideas is to convert one sequence of things into another sequence of things, and thus, this type of neural network can be applied to all sort so of problems, including translating amino acids into 3-dimensional structures.
19.

15:51

Attention for Neural Networks, Clearly Explained!!!

Attention is one of the most important concepts behind Transformers and Large Language Models, like ChatGPT. However, it's not that complicated. In this StatQuest, we add Attention to a basic Sequence-to-Sequence (Seq2Seq or Encoder-Decoder) model and walk through how it works and is calculated, one step at a time. BAM!!!
20.

36:15

Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!

Transformer Neural Networks are the heart of pretty much everything exciting in AI right now. ChatGPT, Google Translate and many other cool things, are based on Transformers. This StatQuest cuts through all the hype and shows you how a Transformer works, one-step-at-a time.
21.

36:45

Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!

Transformers are taking over AI right now, and quite possibly their most famous use is in ChatGPT. ChatGPT uses a specific type of Transformer called a Decoder-Only Transformer, and this StatQuest shows you how they work, one step at a time. And at the end, we talk about the differences between a Normal Transformer and a Decoder-Only Transformer. BAM!
22.

9:40

Tensors for Neural Networks, Clearly Explained!!!

Tensors are super important for neural networks, but can be confusing because different people use the word "Tensor" differently. In this StatQuest, we clear this up and tell you what the big deal is. BAM!
23.

30:01

Essential Matrix Algebra for Neural Networks, Clearly Explained!!!

Although you don't need to know matrix algebra to understand the ideas behind neural networks, if you want to code them or read the latest manuscripts about the field, then you'll need to understand matrix algebra. This video teaches the essential topics in matrix algebra and shows how a neural network can be written as a matrix equation, and then shows how understand PyTorch documentation, error messages and the equations for Attention, which is the fundamental concept behind ChatGPT.
24.

23:43

The matrix math behind transformer neural networks, one step at a time!!!

ransformers, the neural network architecture behind ChatGPT, do a lot of math. However, this math can be done quickly using matrix math because GPUs are optimized for it. Matrix math is also used when we code neural networks, so learning how ChatGPT does it will help you code your own. Thus, in this video, we go through the math one step at a time and explain what each step does so that you can use it on your own with confidence.
25.

23:22

The StatQuest Introduction to PyTorch

PyTorch is one of the most popular tools for making Neural Networks. This StatQuest walks you through a simple example of how to use PyTorch one step at a time. By the end of this StatQuest, you'll know how to create a new neural network from scratch, make predictions and graph the output, and optimize a parameter using backpropagation. BAM!!!
26.

20:43

Introduction to Coding Neural Networks with PyTorch and Lightning

Word embedding is the first step in lots of neural networks, including Transformers (like ChatGPT) and other state of the art models. Here we learn how to code a stand alone word embedding network from scratch and with nn.Linear. We then learn how to load and use pre-trained word embedding values with nn.Embedding.
27.

33:24

Long Short-Term Memory with PyTorch + Lightning

In this StatQuest we'll learn how to code an LSTM unit from scratch and then train it. Then we'll do the same thing with the PyTorch function nn.LSMT(). Along the way we'll learn two cool tricks that Lightning gives us that make our lives easier: 1) How to add more training epochs without starting over and 2) How to easily visualize the training results to determine if you need to do more training or are done.
28.

32:02

Word Embedding in PyTorch + Lightning

Word embedding is the first step in lots of neural networks, including Transformers (like ChatGPT) and other state of the art models. Here we learn how to code a stand alone word embedding network from scratch and with nn.Linear. We then learn how to load and use pre-trained word embedding values with nn.Embedding.
29.

31:11

Coding a ChatGPT Like Transformer From Scratch in PyTorch

In this StatQuest we walk through the code required to code your own ChatGPT like Transformer in PyTorch and we do it one step at a time, with every little detail clearly explained.