This playlist has everything you need to know about Neural Networks, from the basics to the state of the art with Transformers, the foundation of ChatGPT.
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Happy Halloween (Neural Networks Are Not Scary)
I hope everyone has a happy Halloween today! My hope is that even though Neural Networks are not scary, people will stil…
1
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 unders…
2
The Chain Rule, Clearly Explained!!!
The Chain Rule is a method for finding complex derivatives and is used all the time in Statistics and Machine Learning. …
3
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 …
4
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 qu…
5
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…
6
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 …
7
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 …
8
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 …
9
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 …
10
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 …
11
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…
12
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…
13
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 N…
14
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 anoth…
15
Long Short-Term Memory (LSTM), Clearly Explained
Basic recurrent neural networks are great, because they can handle different amounts of sequential data, but even relati…
16
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…
17
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 Spa…
18
Attention for Neural Networks, Clearly Explained!!!
Attention is one of the most important concepts behind Transformers and Large Language Models, like ChatGPT. However, it…
19
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 …
20
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 specif…
21
Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!
Encoder-Only Transformers are the backbone for RAG (retrieval augmented generation), sentiment analysis and classificati…
22
Reinforcement Learning with Neural Networks: Essential Concepts
Reinforcement Learning has helped train neural networks to win games, drive cars and even get ChatGPT to sound more huma…
23
Reinforcement Learning with Neural Networks: Mathematical Details
Here we go through the math required to update a parameter in a neural network using reinforcement learning and we do it…
24
Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!
Generative Large Language Models, like ChatGPT and DeepSeek, are trained on massive text based datasets, like the entire…
25
Tensors for Neural Networks, Clearly Explained!!!
Tensors are super important for neural networks, but can be confusing because different people use the word "Tensor" dif…
26
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 …
27
The matrix math behind transformer neural networks, one step at a time!!!
Transformers, the neural network architecture behind ChatGPT, do a lot of math. However, this math can be done quickly u…
28
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 o…
29
Introduction to Coding Neural Networks with PyTorch and Lightning
Although we've seen how to code a simple neural network with PyTorch, we can make our lives a lot easier if we add Light…
30
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…
31
Word Embedding in PyTorch + Lightning
Word embedding is the first step in lots of neural networks, including Transformers (like ChatGPT) and other state of th…
32
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 on…
