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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 Denis Rothman
Profile icon Denis Rothman
LinkedIn
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.
See other products by 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 Other Books You May Enjoy
Index
Appendix: Answers to the Questions

References

  • Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman, 2019, SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems: https://w4ngatang.github.io/static/papers/superglue.pdf
  • Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman, 2019, GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding: https://arxiv.org/abs/1804.07461
  • Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang, 2019, ERNIE 2.0: A Continual Pretraining Framework for Language Understanding: https://arxiv.org/pdf/1907.12412.pdf
  • Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S. Gordon, 2011, Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning: https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF
  • Richard Socher, Alex...
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Authors (1)

Profile icon Denis Rothman
LinkedIn
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.
See other products by Denis Rothman

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