Email the Author
You can use this page to email Afshine Amidi and Shervine Amidi about Super Study Guide: Transformers & Large Language Models.
About the Book
This book is a concise and illustrated guide for anyone who wants to understand the inner workings of large language models in the context of interviews, projects or to satisfy their own curiosity.
It is divided into 5 parts:
- Foundations: primer on neural networks and important deep learning concepts for training and evaluation
- Embeddings: tokenization algorithms, word-embeddings (word2vec) and sentence embeddings (RNN, LSTM, GRU)
- Transformers: motivation behind its self-attention mechanism, detailed overview on the encoder-decoder architecture and related variations such as BERT, GPT and T5, along with tips and tricks on how to speed up computations
- Large language models: main techniques to tune Transformer-based models, such as prompt engineering, (parameter efficient) finetuning and preference tuning
- Applications: most common problems including sentiment extraction, machine translation, retrieval-augmented generation and many more
This page allows for a self-serve 15% discount (1) aimed at folks who have already purchased the paper version and (2) to account for purchasing power parity across regions. Thank you so much for your interest and support!
About the Authors
Afshine Amidi is currently teaching the Transformers & Large Language Models workshop at Stanford and is also leading LLM efforts at Netflix. Previously, he worked on the Gemini team at Google and used NLP techniques to solve complex queries. Before that, he worked at Uber Eats to improve the quality of the search and recommendation systems. On the side, Afshine published a few papers at the intersection of deep learning and computational biology. He holds a Bachelor’s and a Master’s Degree from École Centrale Paris and a Master’s Degree from MIT.
Shervine Amidi is currently teaching the Transformers & Large Language Models workshop at Stanford and is also working on the Gemini team at Google to leverage LLMs for action-based queries. Previously, he worked on applied machine learning problems for recommender systems at Uber Eats where he focused on representation learning to better surface dish recommendations. On the side, Shervine published a few papers at the intersection of deep learning and computational biology. He holds a Bachelor’s and a Master’s Degree from École Centrale Paris and a Master’s Degree from Stanford University.