1. How I use LLMs The example-driven, practical walkthrough of Large Language Models and their growing list of related features, as a new entry to my general audience series on LLMs. In this more pr 02/28 06:29 ♡
2. Deep Dive into LLMs like ChatGPT This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full training stack of how the mod 02/06 02:23 ♡
3. Let's reproduce GPT-2 (124M) We reproduce the GPT-2 (124M) from scratch. This video covers the whole process: First we build the GPT-2 network, then we optimize its training to be really fast, then we set up t 06/10 07:31 ♡
4. Let's build the GPT Tokenizer The Tokenizer is a necessary and pervasive component of Large Language Models (LLMs), where it translates between strings and tokens (text chunks). Tokenizers are a completely sepa 02/21 01:11 ♡
5. [1hr Talk] Intro to Large Language Models This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What they are, where they are h 11/23 10:27 ♡
6. Let's build GPT: from scratch, in code, spelled out. We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections to ChatGPT, which has ta 01/18 00:33 ♡
7. Building makemore Part 5: Building a WaveNet We take the 2-layer MLP from previous video and make it deeper with a tree-like structure, arriving at a convolutional neural network architecture similar to the WaveNet (2016) fro 11/21 08:32 ♡
8. Building makemore Part 4: Becoming a Backprop Ninja We take the 2-layer MLP (with BatchNorm) from the previous video and backpropagate through it manually without using PyTorch autograd's loss.backward(): through the cross entropy l 10/12 01:56 ♡
9. Building makemore Part 3: Activations & Gradients, BatchNorm We dive into some of the internals of MLPs with multiple layers and scrutinize the statistics of the forward pass activations, backward pass gradients, and some of the pitfalls whe 10/05 00:41 ♡
10. Building makemore Part 2: MLP We implement a multilayer perceptron (MLP) character-level language model. In this video we also introduce many basics of machine learning (e.g. model training, learning rate tunin 09/12 22:43 ♡
