Mastering Transformers - Second Edition: The Journey from BERT to Large Language Models and Stable Diffusion

Yıldırım, Savaş, Chenaghlu, Meysam Asgari-

  • 出版商: Packt Publishing
  • 出版日期: 2024-06-03
  • 售價: $2,050
  • 貴賓價: 9.5$1,948
  • 語言: 英文
  • 頁數: 462
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1837633789
  • ISBN-13: 9781837633784
  • 立即出貨 (庫存=1)

商品描述

Explore transformer-based language models from BERT to GPT, delving into NLP and computer vision tasks, while tackling challenges effectively

Key Features:

- Understand the complexity of deep learning architecture and transformers architecture

- Create solutions to industrial natural language processing (NLP) and computer vision (CV) problems

- Explore challenges in the preparation process, such as problem and language-specific dataset transformation

- Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

Transformer-based language models such as BERT, T5, GPT, DALL-E, and ChatGPT have dominated NLP studies and become a new paradigm. Thanks to their accurate and fast fine-tuning capabilities, transformer-based language models have been able to outperform traditional machine learning-based approaches for many challenging natural language understanding (NLU) problems.

Aside from NLP, a fast-growing area in multimodal learning and generative AI has recently been established, showing promising results. Mastering Transformers will help you understand and implement multimodal solutions, including text-to-image. Computer vision solutions that are based on transformers are also explained in the book. You'll get started by understanding various transformer models before learning how to train different autoregressive language models such as GPT and XLNet. The book will also get you up to speed with boosting model performance, as well as tracking model training using the TensorBoard toolkit. In the later chapters, you'll focus on using vision transformers to solve computer vision problems. Finally, you'll discover how to harness the power of transformers to model time series data and for predicting.

By the end of this transformers book, you'll have an understanding of transformer models and how to use them to solve challenges in NLP and CV.

What You Will Learn:

- Focus on solving simple-to-complex NLP problems with Python

- Discover how to solve classification/regression problems with traditional NLP approaches

- Train a language model and explore how to fine-tune models to the downstream tasks

- Understand how to use transformers for generative AI and computer vision tasks

- Build transformer-based NLP apps with the Python transformers library

- Focus on language generation such as machine translation and conversational AI in any language

- Speed up transformer model inference to reduce latency

Who this book is for:

This book is for deep learning researchers, hands-on practitioners, and ML/NLP researchers. Educators, as well as students who have a good command of programming subjects, knowledge in the field of machine learning and artificial intelligence, and who want to develop apps in the field of NLP as well as multimodal tasks will also benefit from this book's hands-on approach. Knowledge of Python (or any programming language) and machine learning literature, as well as a basic understanding of computer science, are required.

商品描述(中文翻譯)

探索從BERT到GPT的基於Transformer的語言模型,深入研究自然語言處理(NLP)和計算機視覺任務,並有效應對挑戰。

主要特點:
- 理解深度學習架構和Transformer架構的複雜性
- 解決工業自然語言處理(NLP)和計算機視覺(CV)問題
- 探索準備過程中的挑戰,如問題和特定語言的數據集轉換
- 購買印刷版或Kindle書籍包括免費的PDF電子書

書籍描述:
BERT、T5、GPT、DALL-E和ChatGPT等基於Transformer的語言模型主導了NLP研究,成為一種新的範式。由於它們準確且快速的微調能力,基於Transformer的語言模型已能夠在許多具有挑戰性的自然語言理解(NLU)問題上超越傳統的基於機器學習的方法。

除了NLP,最近還建立了一個快速增長的多模態學習和生成式AI領域,展示了有希望的結果。掌握Transformer將幫助您理解並實施多模態解決方案,包括文本到圖像的轉換。書中還解釋了基於Transformer的計算機視覺解決方案。您將首先了解各種Transformer模型,然後學習如何訓練不同的自回歸語言模型,如GPT和XLNet。本書還將使您迅速掌握提高模型性能以及使用TensorBoard工具包跟踪模型訓練的方法。在後面的章節中,您將專注於使用視覺Transformer解決計算機視覺問題。最後,您將發現如何利用Transformer模型對時間序列數據進行建模和預測。

通過閱讀本書,您將了解Transformer模型以及如何使用它們來解決NLP和CV中的挑戰。

學到什麼:
- 專注於使用Python解決從簡單到複雜的NLP問題
- 發現如何使用傳統NLP方法解決分類/回歸問題
- 訓練語言模型,並探索如何對下游任務進行微調
- 理解如何使用Transformer進行生成式AI和計算機視覺任務
- 使用Python transformers庫構建基於Transformer的NLP應用程序
- 專注於語言生成,如機器翻譯和對話AI
- 加快Transformer模型的推理速度,減少延遲

本書適合深度學習研究人員、實踐者和ML/NLP研究人員。教育工作者以及具有良好的編程能力、機器學習和人工智能領域知識,並希望在NLP和多模態任務領域開發應用程序的學生也將從本書的實踐方法中受益。需要具備Python(或任何編程語言)和機器學習文獻的知識,以及對計算機科學的基本理解。