A Generative Journey to AI Mastering the foundations and frontiers of generative deep learning (Ramchandani, Toni) (Z-Library)

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A Generative Journey to AI Mastering the foundations and frontiers of generative deep learning Toni Ramchandani www.bpbonline.com

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First Edition 2025 Copyright © BPB Publications, India ISBN: 978-93-65890-846 All Rights Reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication, photocopy, recording, or by any electronic and mechanical means. LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY The information contained in this book is true to correct and the best of author’s and publisher’s knowledge. The author has made every effort to ensure the accuracy of these publications, but publisher cannot be held responsible for any loss or damage arising from any information in this book. All trademarks referred to in the book are acknowledged as properties of their respective owners but BPB Publications cannot guarantee the accuracy of this information. www.bpbonline.com

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Dedicated to My nurturing parents Rajan Ejna My beloved wife Neelu My precious children Hriti Nihit

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About the Author Toni Ramchandani is a visionary leader and an AI Research Engineer who is always Driving Excellence as Tech Delivery Head with a distinguished career that spans multiple facets of technology and innovation. He currently serves as the Vice President at MSCI Inc. and is renowned for his expertise in generative AI, QA, DevSecOps, Databricks, cloud technologies, and more. His role involves spearheading cutting-edge technology initiatives and leading teams towards achieving outstanding results in a highly competitive environment. Toni built his academic foundation at G H Raisoni College of Engineering in Nagpur, where he gained a solid grounding in engineering principles. Over the years, he has broadened his knowledge and skills, becoming a prominent figure in the fields of AI and cloud technology. His deep understanding of artificial intelligence has allowed him to explore the frontiers of Generative AI, applying machine learning techniques to develop innovative solutions that make a real-world impact. Beyond his corporate responsibilities, he is a passionate advocate for sharing knowledge and learning continuously. He is a sought-after conference speaker, where he shares his insights on the latest trends in AI, ML, QA, DevSecOps, and cloud computing. As a corporate trainer, he has trained numerous professionals, helping them to upskill and stay ahead in the rapidly evolving tech landscape. His commitment to education is also evident in his work as an author, where he writes about complex technological concepts, making them accessible to a broader audience. A

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resident of Pune, Maharashtra, India, Toni’s life is not just about technology. He is driven by a deep love for sports, adventure, and innovation. These passions fuel his professional endeavors, giving him the energy and creativity to tackle challenges and inspire those around him. Whether he is exploring new technological solutions or pushing the limits in sports and adventure, Toni approaches every challenge with a can-do attitude and a relentless drive for excellence.

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About the Reviewers ❖ Ameya Deshpande is an expert in the field of Software engineering with over a decade of experience in the tech and automotive industries, specializing in mobile app development and generative AI. He played a key role in launching the Pixel Studio app on Google Pixel, a pioneering text-to-image generation application for mobile devices. A passionate advocate for user safety and trust, Ameya has worked to ensure Google products prioritize privacy and build strong relationships with users worldwide. Ameya is currently working at Google and he has been a pivotal member of the teams launching flagship Pixel devices worldwide. ❖ Sai Teja is Data Science professional with over 9+ years of extensive experience in Advanced NLP, MLOps, Generative AI, Large Language Models, and solving complex business problems using Machine Learning and AI models. He specializes in Generative AI and advanced NLP solutions, leveraging his expertise to deliver tailored AI/ML solutions that drive innovation and efficiency for clients. He is currently working as a Senior Group Manager (AVP) at WNS Global Services, where he spearheads the GenAI solutions, capabilities and proof of concepts team within the WNS Analytics Products Division. Passionate about AI strategy and product development, he continuously researches and implements state-of-the-art AI

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methodologies to optimize performance and deliver high- quality, future-proof solutions.

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Acknowledgement I want to extend my deepest appreciation to my family and friends for their steadfast support and encouragement during the writing of this book. My heartfelt thanks go to my wife Neelu, whose love, patience, and constant support have been my anchor throughout this journey. A special thanks goes to my parents Rajan and Ejna, whose resilience and hard work through challenging times have laid the foundation for all that I have achieved. I am also thankful for the invaluable contributions of my children Hriti and Nihit. Their infectious energy and the joy they bring into my life have been a constant source of motivation, reminding me to find a balance between work and family. My gratitude also extends to BPB Publications for their expertise and guidance, which were crucial in bringing this book to life. The journey of revising this work was long and challenging, but it was made possible through the dedicated efforts of reviewers, technical experts, and editors who contributed greatly to its completion. I also want to acknowledge my colleagues and co-workers from the tech industry. Their insights and feedback have enriched my understanding and influenced this book in significant ways. Lastly, I am deeply grateful to all the readers who have shown interest in my book. Your support has been

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instrumental in turning this project into a reality and for that, I am truly thankful.

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Preface As generative AI models continue to break new ground, understanding the principles and practices that underlie these technologies is more critical than ever. This book is designed to provide a comprehensive guide to deep learning, with a special emphasis on its applications in generative AI. It covers a wide range of topics, from the foundational concepts of deep learning and neural networks to advanced techniques in generative models, such as GANs, VAEs, transformers, and more. Throughout the book, you will explore the key features of these models and learn how to leverage them to build systems that generate text, images, music, and more. We also explore best practices for training and evaluating these models, ensuring they are both robust and effective in real- world applications. Numerous practical examples are provided to help you grasp these concepts and apply them in your work. This book is intended for anyone interested in deep learning and generative AI, whether you are just beginning your journey or looking to deepen your expertise. Whether you are a researcher, developer, or enthusiast, this book will equip you with the knowledge and skills needed to create innovative AI solutions that push the boundaries of what is possible. Chapter 1: Introduction to Deep Learning – This chapter introduces deep learning through the lens of generative AI, covering its evolution, fundamental

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principles, and real-world applications. This chapter also explains the mathematical foundations and contrasts deep learning with traditional machine learning approaches. Chapter 2: Neural Networks and Deep Learning Architectures – This chapter introduces the building blocks of deep learning, including neurons, layers, and activation functions. The chapter further explores various architectures, such as MLPs, RNNs, CNNs, and autoencoders, and introduces the concept of generative models. Chapter 3: Unveiling Generative Models – This chapter introduces a detailed overview of generative models, their taxonomy, and the distinctions between generative and discriminative models. This chapter also covers the mathematical underpinnings of these models and discusses how to select and evaluate the right model for specific tasks. Chapter 4: Generative Adversarial Networks – This chapter introduces the fundamentals of GANs, their advantages and disadvantages, and various GAN variants. Practical examples of training and implementing GANs, including Vanilla GAN and Deep Convolutional GAN, are also provided. Chapter 5: Variational Autoencoders – This chapter introduces VAEs, covering their history, architecture, and key concepts like the reparameterization trick. The chapter compares GANs and VAEs, explores their applications, and discusses the fusion of GANs and VAEs. Chapter 6: Diffusion Models – This chapter introduces diffusion models, including their history and variants. The chapter also covers denoising diffusion models, their applications, and comparisons with GANs and VAEs, including practical examples like Stable Diffusion.

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Chapter 7: Transformers and Large Language Models – This chapter introduces an in-depth look at transformers, their architecture, and training processes. The chapter also covers LLMs, like BERT and GPT, along with practical implementations and emerging variants, such as Vision Transformers and T5. Chapter 8: Exploring Generative Models – This chapter introduces a specific generative model, including Pix2Pix, CycleGANs, StyleGAN, and others. This chapter also explores autoregressive models, energy-based models, and normalizing flow models. Chapter 9: Video and Music Generation – This chapter introduces the evolution and fundamentals of video and music generation, detailing various models and applications in these creative domains. The chapter covers state-of-the- art video generation techniques and music models, like MuseGAN and MIDINet. Chapter 10: Artistic Side of Generative AI – This chapter introduces the creative applications of AI, including DeepDream, neural style transfer, and deepfakes. The chapter also covers generating 3D structures and using AI in gaming. Chapter 11: Ethics, Challenges, and Future – This chapter introduces the ethical considerations of generative AI, including historical context, case studies, and global regulatory frameworks. The chapter also discusses AI hallucinations and the road ahead for responsible AI.

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Code Bundle and Coloured Images Please follow the link to download the Code Bundle and the Coloured Images of the book: https://rebrand.ly/fff69e The code bundle for the book is also hosted on GitHub at https://github.com/bpbpublications/A-Generative- Journey-to-AI. In case there’s an update to the code, it will be updated on the existing GitHub repository. We have code bundles from our rich catalogue of books and videos available at https://github.com/bpbpublications. Check them out! Errata We take immense pride in our work at BPB Publications and follow best practices to ensure the accuracy of our content to provide with an indulging reading experience to our subscribers. Our readers are our mirrors, and we use their inputs to reflect and improve upon human errors, if any, that may have occurred during the publishing processes involved. To let us maintain the quality and help us reach out to any readers who might be having difficulties due to any unforeseen errors, please write to us at : errata@bpbonline.com Your support, suggestions and feedbacks are highly appreciated by the BPB Publications’ Family.

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Table of Contents 1. Introduction to Deep Learning Introduction Structure Objectives Deep learning from generative AI lens Understanding deep learning The evolution of deep learning Mathematics for deep learning Calculus in deep learning Probability theory in deep learning Machine learning Types of machine learning Supervised learning Unsupervised learning Reinforcement learning Deep learning vs. traditional machine learning Real-world applications of deep learning Conclusion 2. Neural Networks and Deep Learning Architectures Introduction Structure Objectives

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Understanding neurons and layers Biological neuron inspiration Activation functions and their importance Types of activation functions Activation functions visualization Forward propagation in neural networks Importance of feedforward networks Steps in forward propagation The concept of backpropagation Training neural networks Multilayer Perceptron Training MLPs Overfitting Convolutional neural networks Shared weights Shared biases Recurrent neural networks SimpleRNN implementation Autoencoders Introduction to generative models The quest for creativity Bridging the gap with reality Unleashing imagination Practical applications The intricacies of training Fueling curiosity and exploration Conclusion 3. Unveiling Generative Models

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Introduction Structure Objectives Understanding generative models Taxonomy of generative models Selecting the right model Basic generative model Discriminative models Types of discriminative models Generative models vs. discriminative models Mathematical notation Bayes’ theorem in action Parameter estimation and learning Combining generative and discriminative models Applications of generative models Applications of discriminative models Latent space Evaluating generative models Bias in generative AI models Conclusion 4. Generative Adversarial Networks Introduction Structure Objectives Introduction to GANs Advantages of GANs Disadvantages of GANs