Our Stanford class "CME 295: Transformers & Large Language Models" is now available on YouTube!

Stanford University released the best cheatsheets you'll ever find to learn LLMs & Transformers! These cheatsheets cover: • Self-attention, Flash Attention, LoRA, SFT • Mixture of Experts, Distillation, Quantization • RAG, Agents, LLM-as-a-judge 100% Free and Open Source

Geminiの開発エンジニアが書いたスタンフォード講義資料「Transformer と大規模言語モデル」の日本語版 by @yoshiyukinakai これはがっつり読みたい。書籍紹介ページ superstudy.guide/transformer… と、無償のチートシートもある。github.com/afshinea/stanford…

Transformers & LLMs cheatsheets for Stanford's CME-295! Covering tokenization, self-attention, prompting, fine-tuning, LLM-as-a-judge, RAG, AI Agents, and reasoning models. 100% free and open-source.

Book Review Video : Super Study Guide: Transformers & Large Language Models by @afshinea & @shervinea 🙂 Video Link : youtube.com/watch?v=VLTj3Se3… #LLM #GenerativeAI #DataScience #MachineLearning #NLP

Transformers and LLMs have revolutionized AI by dramatically improving how machines understand and generate language. They excel at capturing context and meaning in text, enabling breakthroughs in applications like chatbots, translation, coding, and creative content generation. If you are looking to get into LLMs, then "Super Study Guide: Transformers & Large Language Model" by @shervinea and @afshinea is a must have study companion. The book is structured into five comprehensive parts: 1. Foundations: Offers a primer on neural networks and essential deep learning concepts related to training and evaluation. 2. Embeddings: Delves into tokenization algorithms, word embeddings like word2vec, and sentence embeddings using RNNs, LSTMs, and GRUs. 3. Transformers: Explores the motivation behind the self-attention mechanism, provides a detailed overview of the encoder-decoder architecture, and discusses variations such as BERT, GPT, and T5. It also includes tips on optimizing computations. 4. Large Language Models: Covers key techniques for tuning transformer-based models, including prompt engineering, parameter-efficient fine-tuning, and preference tuning. 5. Applications: Addresses common challenges like sentiment extraction, machine translation, and retrieval-augmented generation. This concise, illustrated guide is designed to deepen your understanding of LLMs, making it ideal for interviews, projects, or personal enrichment.

Folks although i have written books on machine learning (although not specifically on NLP but planning to) but this book is the best! This is not sponsored i purchased it with my own money This book is good because: - There is no other book which is as up to date - There is no book that has such good illustrations - This book does not have any extra irrelevant info - No excessive maths Here is the link to the book - superstudy.guide/transformer… Thanks to the author for such a nice book - @shervinea @afshinea

If you are looking to have a better understanding of LlMs and how they (really) work, do yourself a favor and get this book by @afshinea and @shervinea. It is amazing! It covers all the inportant concepts behind the Transformer architecture with deep learning foundations, tokenization techniques (what they are and how they are made), embeddings, fine-tuning and more. It’s of course packed with useful info and reminders for us in the field, but most importantly, it has one of the best and highest concentration of visual explanations of all time (I’d even say on par with the famous (also amazing) visuals of @JayAlammar’s visual transformer). If you feel like you are a “visual learner”, this one is for you. It’s also a really nice addition to the “Building LLMs for Production” book we released since this one does not focus on code but rather lays a nice and solid theoretical foundation that I still believe to be important for developing true expertise. Thanks for that amazing effort and book Afshine Amidi, Shervine Amidi :) #llms #ai #book

Really enjoying this visual study guide on Transformers and LLMs. It contains a very concise overview of the key concepts in Transformers and LLMs. Topics range from embeddings to attention mechanism to post-training techniques. Thanks for the great book @shervinea and @afshinea!