Overview

This book is a comprehensive, practical, and systematic guide designed to take you from the fundamental concepts of neural networks to building and deploying sophisticated Generative AI applications. It is engineered for learners who prefer a hands-on, ""learn-by-doing"" approach over abstract theoretical discourse. Philosophy The guiding philosophy of this book is ""construction over theory."" I believe the most profound understanding of a complex system comes from building it from its elementary parts. Instead of just describing what a Transformer model is, we will build the essential components like the self-attention mechanism step-by-step. This ""from scratch"" approach demystifies the technology, transforming abstract concepts into tangible, implementable code. The focus is squarely on the how-how to design the architecture, how to implement the algorithm, and how to develop a functional application. Key Features 1. Strictly Practical Orientation: Over 70% of the content is focused on implementation, code, and application development. 2. Simplified Algorithms: All algorithms are presented in their simplest effective form, making them accessible to beginners without sacrificing core functionality. 3. Step-by-Step Implementation: Each project and example is broken down into clear, sequential steps from data preparation to model evaluation. 4. Application-Centric Chapters: Chapters are dedicated to building specific types of generative applications (text, image, multimodal), reflecting industry use cases. 5. End-to-End Capstone Project: A complete, working DIY project in the final chapter provides an invaluable, hands-on learning experience. 6. Latest and Relevant Topics: Covers modern, state-of-the-art architectures like Transformers and Diffusion Models, ensuring the content is current and valuable. Key Takeaways 1. Upon completing this book, you will be able to: 2. Understand and implement the core components of various generative models. 3. Build text generation applications using LSTMs and Transformers. 4. Develop image generation systems using GANs and Diffusion Models. 5. Design and implement multimodal AI applications that process both text and images. 6. Fine-tune pre-trained models for specific tasks. 7. Package and deploy your Generative AI models as web services. 8. Confidently approach and solve complex problems using Generative AI techniques. Disclaimer: Earnest request from the Author. Kindly go through the table of contents and refer kindle edition for a glance on the related contents. Thank you for your kind consideration!

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