Build Real-World AI with Transformers Powered by PyTorch 2.0.
Key Features
? Complete hands-on projects spanning NLP, vision, and speech AI.
? Interactive Jupyter Notebooks with real-world industry scenarios.
? Build a professional AI portfolio ready for career advancement.
Book Description
Transformer models have revolutionized AI across natural language processing, computer vision, and speech recognition. "Ultimate Transformer Models Using PyTorch 2.0" bridges theory and practice, guiding you from fundamentals to advanced implementations with hands-on projects that build a professional AI portfolio.
This comprehensive journey spans 11 chapters, beginning with transformer foundations and PyTorch 2.0 setup. With this book, you will master self-attention mechanisms, tackle NLP tasks such as text classification and translation, and then expand into computer vision and speech processing. Advanced topics include BERT and GPT models, the Hugging Face ecosystem, training strategies, and deployment techniques. Each chapter features practical exercises that reinforce learning through real-world applications.
By the end of this book, you will be able to confidently design, implement, and optimize transformer models for diverse challenges. So, whether revolutionizing language understanding, advancing computer vision, or innovating speech recognition, you will possess both theoretical knowledge and practical expertise to deploy solutions effectively across industries like healthcare, finance, and social media, positioning yourself at the AI revolution's forefront.
What you will learn
? Build custom transformer architectures from scratch, using PyTorch 2.0.
? Fine-tune BERT, GPT, and T5 models for specific applications.
? Deploy production-ready AI models across NLP, vision, and speech domains.
? Master Hugging Face ecosystem for rapid model development and deployment.
? Optimize transformer performance, using advanced training techniques and hyperparameters.
? Create a professional portfolio showcasing real-world transformer implementations.
Table of Contents
1. Understanding the Evolution of Neural Networks
2. Fundamentals of Transformer Architecture
3. Getting Started with PyTorch 2.0
4. Natural Language Processing with Transformers
5. Computer Vision with Transformers
6. Speech Processing with Transformers
7. Advanced Transformer Models
8. Using HuggingFace with PyTorch
9. Training and Fine-Tuning Transformers
10. Deploying Transformer Models
11. Transformers in Real-World Applications
Index
About the Authors
Abhiram Ravikumar is a Senior Data Scientist at Publicis Sapient, where he applies his extensive expertise in natural language processing, machine learning, and AI to solve complex business challenges. He holds a Master's degree in Data Science from King's College, London, and brings a wealth of academic and industry experience to this book on transformer models and PyTorch 2.0.
An experienced member of the Mozilla Tech Speakers program, Abhiram has presented at international tech conferences such as PyCon, MozFest, and CodeMash. His ability to communicate complex technical concepts is further evidenced by his LinkedIn Learning course on Rust Programming, which has reached over 60,000 learners. His talk on Clustering Topic Models at the Analytics Vidhya DataHour Forum Talk series attracted over 4,200 participants and received an impressive feedback rating of 4.6 out of 5.
