Super Study Guide

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

Year

2024

Language

English

Publisher

Independently published

Editions

1

Pages

247

ISBN

9798836693312

Data source: Wikidata