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Transformers for Natural Language Processing and Computer Vision - Third Edition

You're reading from Transformers for Natural Language Processing and Computer Vision - Third Edition

Product type Book

Published in Feb 2024

Publisher Packt

ISBN-13 9781805128724

Pages 728 pages

Edition 3rd Edition

Languages

Concepts

Author (1):

Denis Rothman

Table of Contents (24) Chapters

Preface

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

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

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

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

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Index

Appendix: Answers to the Questions

References

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin, 2017, Attention Is All You Need: https://arxiv.org/abs/1706.03762
Alex Warstadt, Amanpreet Singh, Samuel R. Bowman, 2018, Neural Network Acceptability Judgments: https://arxiv.org/abs/1805.12471
The Corpus of Linguistic Acceptability (CoLA): https://nyu-mll.github.io/CoLA/
Documentation on Hugging Face models:

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Authors (1)

Denis Rothman

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.

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