-
Book Overview & Buying
-
Table Of Contents
Mastering Transformers - Second Edition
By :
Mastering Transformers
By:
Overview of this book
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.
Table of Contents (25 chapters)
Preface
Part 1: Recent Developments in the Field, Installations, and Hello World Applications
Free Chapter
Chapter 1: From Bag-of-Words to the Transformers
Chapter 2: A Hands-On Introduction to the Subject
Part 2: Transformer Models: From Autoencoders to Autoregressive Models
Chapter 3: Autoencoding Language Models
Chapter 4: From Generative Models to Large Language Models
Chapter 5: Fine-Tuning Language Models for Text Classification
Chapter 6: Fine-Tuning Language Models for Token Classification
Chapter 7: Text Representation
Chapter 8: Boosting Model Performance
Chapter 9: Parameter Efficient Fine-Tuning
Part 3: Advanced Topics
Chapter 10: Large Language Models
Chapter 11: Explainable AI (XAI) in NLP
Chapter 12: Working with Efficient Transformers
Chapter 13: Cross-Lingual and Multilingual Language Modeling
Chapter 14: Serving Transformer Models
Chapter 15: Model Tracking and Monitoring
Part 4: Transformers beyond NLP
Chapter 16: Vision Transformers
Chapter 17: Multimodal Generative Transformers
Chapter 18: Revisiting Transformers Architecture for Time Series
Index
