Transformers for Natural Language Processing and Computer Vision - Third Edition

Discover the fundamental principles and practical applications of transformer models with this comprehensive guide. With step-by-step examples, this book covers topics including pretraining, fine-tuning, and the deployment of cutting-edge models to tackle real-world NLP and computer vision problems effectively.

What this Book will help me do

• Understand the architecture and functionality of transformers, including BERT, GPT, ViT, and DALL-E.
• Gain practical skills in fine-tuning and pretraining transformer models for custom applications.
• Learn to implement retrieval-augmented generation and other techniques to manage and improve LLM outputs.
• Acquire knowledge about the challenges and limitations of large language models and approaches to mitigate them.
• Explore applications in both natural language processing and computer vision through generative models.

Author(s)

Denis Rothman, an experienced practitioner and author in the field of artificial intelligence, has written extensively on neural networks, cognitive science, and practical applications of advanced AI models. Known for his ability to demystify complex concepts, Denis combines academic rigour with a hands-on teaching approach.

Who is it for?

This book is targeted towards engineers, data scientists, and developers who are eager to deepen their understanding of transformer models and their applications in the fields of NLP and CV. Readers should have a basic understanding of machine learning and Python, but the book is also accessible to curious learners from other domains, thanks to its clear tutorials and examples. Whether you're looking to gain competence in working with LLMs or aiming to innovate in AI-driven projects, this book offers valuable insights. Join the frontier of modern AI development with this resource.