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The Art and Science of Transformer: A Breakthrough in the Modern AI and NLP
Are you ready to dive deep into the world of AI and unlock the secrets of one of the most revolutionary advancements in natural language processing? This book is your definitive guide. Whether you are a student, an aspiring data scientist, or a professional looking to expand your knowledge, this book aims to make the complex world of transformers accessible and understandable with its comprehensive coverage, clear explanations, and insightful guidance.
What You Will Learn:
Token Embedding: Grasp the basics of representing words or tokens in vector space, setting the stage for deeper understanding.
Attention Mechanism: Discover how attention mechanisms enable models to focus on relevant parts of input data, enhancing performance.
Self-Attention: Learn about self-attention and its pivotal role in allowing models to weigh the importance of different words within a sequence.
Positional Encoding: Understand how positional encoding helps transformers retain the order of words, a crucial aspect of sequence processing.
Multi-Headed Attention: Dive into the concept of multi-headed attention and its contribution.
Transformer Architecture: Explore the complete transformer architecture, from encoder and decoder stacks to the whole architecture.
GPT and BERT Architecture: Explore how these models utilize Transformer architecture to perform tasks like text generation, sentiment analysis, and more.
