AI Book Club: Transformers for Natural Language Processing and Computer Vision
August's book is "Transformers for Natural Language Processing and Computer Vision"!
This is a casual-style audio discussion event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back.
Want to discuss the contents during the rest of the month? Join Flyte AI & MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/
Feel free to join the discussion even if you have not read the book chapters! :)
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About the book:
Title: Transformers for Natural Language Processing and Computer Vision
Authors: Denis Rothman
Published: February 2024
Read at your own pace, but my goal will be these chapters per week:
Week 1:
What Are Transformers?
Getting Started With The Architecture Of The Transformer Model
Emergent Vs Downstream Tasks: The Unseen Depths Of Transformers
Advancements In Translations With Google Trax, Google Translate, And Gemini
Diving Into Fine-Tuning Through BERT
Week 2
Pretraining A Transformer From Scratch Through RoBERTa
The Generative AI Revolution With ChatGPT
Fine-Tuning OpenAI GPT Models
Shattering The Black Box With Interpretable Tools
Investigating The Role Of Tokenizers In Shaping Transformer Models
Week 3
Leveraging LLM Embeddings As An Alternative To Fine-Tuning
Toward Syntax-Free Semantic Role Labeling With ChatGPT And GPT-4
Summarization With T5 And ChatGPT
Exploring Cutting-Edge LLMs With Vertex AI And PaLM 2
Guarding The Giants: Mitigating Risks In Large Language Models
Week 4
Beyond Text: Vision Transformers In The Dawn Of Revolutionary AI
Transcending The Image-Text Boundary With Stable Diffusion
Hugging Face AutoTrain: Training Vision Models Without Coding
On The Road To Functional AGI With HuggingGPT And Its Peers
Beyond Human-Designed Prompts With Generative Ideation
Discuss as you read in the ai-book-club channel: https://slack.flyte.org/
Book Description:
Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV).
The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You’ll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs.
Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication.
This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.
What you will learn
Breakdown and understand the architectures of the Original Transformer, BERT, GPT models, T5, PaLM, ViT, CLIP, and DALL-E
Fine-tune BERT, GPT, and PaLM 2 models
Learn about different tokenizers and the best practices for preprocessing language data
Pretrain a RoBERTa model from scratch
Implement retrieval augmented generation and rules bases to mitigate hallucinations
Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP
Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V
Who this book is for
This book is ideal for NLP and CV engineers, software developers, data scientists, machine learning engineers, and technical leaders looking to advance their LLMs and generative AI skills or explore the latest trends in the field. Knowledge of Python and machine learning concepts is required to fully understand the use cases and code examples. However, with examples using LLM user interfaces, prompt engineering, and no-code model building, this book is great for anyone curious about the AI revolution.
Learn more about the book here:
https://learning.oreilly.com/library/view/transformers-for-natural/9781805128724/