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Key benefits

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

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.

Who is this book 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.

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

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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Customer reviews

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DEVASSYJP Aug 21, 2024

Transformer-based language models like BERT, T5, GPT, DALL-E, and ChatGPT have revolutionized natural language processing (NLP) by outperforming traditional machine learning methods in complex natural language understanding (NLU) tasks. This book explores the power of Transformers beyond NLP, diving into the rapidly growing fields of multimodal learning and generative Al with impressive results.Readers will learn to implement multimodal solutions, including text-to-image generation, and will understand the fundamentals of various transformer models. The book also covers training autoregressive language models like GPT and XLNet, boosting model performance, and tracking model training using TensorBoard. Later chapters focus on using vision transformers for computer vision problems and applying transformers to model time series data and predictions.By the end, readers will have a strong grasp of transformer models and their applications in solving challenges across NLP and computer vision. This book is an invaluable resource for those looking to enhance their expertise in thesecutting-edge technologies.

Amazon Verified review

Rohan Pandit Jun 09, 2024

Transformer-based language models like BERT, T5, GPT, DALL-E, and ChatGPT have revolutionized natural language processing (NLP) by outperforming traditional machine learning methods in complex natural language understanding (NLU) tasks. This book explores the power of Transformers beyond NLP, diving into the rapidly growing fields of multimodal learning and generative AI with impressive results.Readers will learn to implement multimodal solutions, including text-to-image generation, and will understand the fundamentals of various transformer models. The book also covers training autoregressive language models like GPT and XLNet, boosting model performance, and tracking model training using TensorBoard. Later chapters focus on using vision transformers for computer vision problems and applying transformers to model time series data and predictions.By the end, readers will have a strong grasp of transformer models and their applications in solving challenges across NLP and computer vision. This book is an invaluable resource for those looking to enhance their expertise in these cutting-edge technologies.

Amazon Verified review

Soni Raju Jun 26, 2024

This book explores what readers will learn to implement multimodal solutions, including text-to-image generation, and will understand the fundamentals of various transformer models. The book also covers training autoregressive language models like GPT and XLNet, boosting model performance, and tracking model training using TensorBoard. In the end, readers will have a strong grasp of transformer models and their applications in solving challenges across NLP and computer vision. This book is an invaluable resource.

Amazon Verified review

The fan is incredible with 4 different variations of air speed. Perfect for outdoors. You can just clip it on shirt or pants and you are good to go. Loved it Aug 14, 2024

This book goes through the evolution of Natural Language Processing (NLP), from traditional methods to the cutting-edge Transformer architecture. It serves as an invaluable resource for professionals and enthusiasts in machine learning, deep learning, and NLP.Key Highlights:1. From Basics to Advanced: The book starts with the foundational concepts, making it accessible for beginners, and progresses to advanced topics, ensuring that even seasoned professionals find value.2. Hands-On Approach: The authors provide practical examples and code snippets, allowing readers to experiment and implement concepts in real-time.3. Comprehensive Coverage: Topics like autoencoding language models, generative models, fine-tuning for various tasks, and the latest advancements in large language models are covered in-depth.4. Multimodal Learning: The book explores the exciting realm of multimodal learning, discussing how Transformers can be used beyond NLP, such as in computer vision and generative AI.5. Future-Proofing: With a detailed look at efficient Transformers and parameter-efficient fine-tuning, the book prepares you for the future of scalable and sustainable AI models.Whether you're a researcher, practitioner, or educator in the AI field, this book is a must-read to stay ahead in the rapidly evolving landscape of NLP and Transformers.

Amazon Verified review

Banachan Aug 26, 2024

I have the 1st edition of the book and this 2nd edition is a great update to the first one to keep up with the times. If you enjoyed the 1st ed, then you don't want to miss this second update. Lots of new information given the new and rapid developments the LLM space has gone through. It continues to be a great comprehensive guide to understanding and implementing Transformer models in various artificial intelligence (AI) tasks. It provides a deep dive into the architecture of Transformers, showcasing their effectiveness across a range of areas including Natural Language Processing (NLP), computer vision, time series analysis, and multimodal tasks.It goes more into details on how Transformers is applied to time series data, covering the fundamental concepts of time series and demonstrating how it can be effectively utilized for these types of data, which many models struggle with due to their complexity. It goes into the ubiquity and superiority in handling diverse tasks compared to other architectures and their pivotal role in the rise of Generative AI within both industry and academic communities. A fun read at that.It has quite a comprehensive coverage, providing an in-depth look at the theory and application across different domains, making it a valuable resource for those interested in understanding various aspects of this powerful model architecture. There are ample practical examples through step-by-step guidance which aids in the understanding and implementation of concepts discussed. Some cutting-edge Content to keep up with the times, like current and emerging trends in generative tasks and multimodal learning.There are recent topics in this field that I thought would have been nice if included, like topics on ethics on Gen AI, deployment scenarios, emergence of small language models and what it means, how RLHF plays more into the area, how it can work with quantum computing, etc. While I am a proponent of deep dives, beginners to transformers might not get the more generalized explanations from scratch. I figured it can't be all things to all people. But an overall great read.

Amazon Verified review

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

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