I’ve seen several examples on this subreddit of people interested in in-depth explanations of how Transformers and LLMs work.

This is a gap that my twin brother and I have been trying to fill for the past 3 1/2 years. Last week, we published “Super Study Guide: Transformers & Large Language Models,” a 250-page book with over 600 illustrations aimed at visual learners who have a strong interest in going into the field.

This book covers the following topics in detail:

• Foundations: introduction to neural networks and key deep learning concepts for training and evaluation.
• Embeddings: tokenization algorithms, word embeddings (word2vec) and sentence embeddings (RNN, LSTM, GRU).
• Transformers: motivation behind the self-attention mechanism, detailed overview of the encoder-decoder architecture and related variations such as BERT, GPT and T5, along with tips and tricks to speed up computations.
• Large language models: main techniques for tuning transformer-based models, such as prompt engineering, (parameter-efficient) fine-tuning and preference tuning.
• Applications: The most common problems include sentiment extraction, machine translation, retrieval-augmented generation and many more.

(In case you’re wondering, this content is in the same vein as the Stanford illustrated study guides we shared on this subreddit 5-6 years ago for CS 229: Machine Learning, CS 230: Deep Learning, and CS 221: Artificial Intelligence)

Have fun learning!

https://preview.redd.it/n6zraaltemjd1.jpg?width=1905&format=pjpg&auto=webp&s=1110f750df0d8a60d5fdf1d4967b41e1b5617efe