7 Must-Read Generative AI Books
Generative AI has gained significant attention in 2023. As everyone is busy experimenting with it and building innovative applications and tools for the betterment of humanity, it becomes increasingly more important to understand the basics and technical nuances, and not just fall prey for the hype. Here AIM has listed the top seven must read generative AI books of 2023 for machine learning engineers and data scientists, enhancing your understanding and skills in the field of Generative AI. Table of contentsGenerative AI with Python and TensorFlow 2Generative Deep LearningGenerative AI with LangChainGenerative AI on AWSArtificial Intelligence & Generative AI for BeginnersGenerative AI in PracticeThe Equalizing Quill Generative AI with Python and TensorFlow 2 by Joseph Babcock and Raghav Bali In this book, Generative AI with Python and TensorFlow 2 by Joseph Babcock and Raghav Bali gives you a glimpse of generative models evolution, from Boltzmann machines to VAEs and GANs, learn TensorFlow model implementation, and stay updated on deep neural network research. Access the Book here. Generative Deep Learning By David Foster  (Author) & Karl Friston (Foreword) Generative Deep Learning by David Foster and Karl Friston talks about machine learning engineers and data scientists how to create generative deep learning models using TensorFlow and Keras, including VAEs, GANs, Transformers, normalizing flows, energy-based models, and denoising diffusion models. It covers deep learning basics and advanced architectures, providing tips for efficient learning and creativity. Access the book here. Generative AI with LangChain By BenAuffarth Generative AI with LangChain by Ben Auffarth explores the functions, capabilities, and limitations of LLR models like ChatGPT and Bard, and how to use the LangChain framework for production-ready applications. It covers transformer models, attention mechanisms, training and fine-tuning, data-driven decision-making, automated analysis and visualization using pandas and Python, and heuristics for model usage. The goal is to provide a comprehensive understanding of LLMs and their potential for enhancing our understanding of the world. Access the book here. Generative AI on AWS by Chris Fregly, Antje Barth, Shelbee Eigenbrode You’ll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you’ll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Access the book here. Artificial Intelligence & Generative AI for Beginners by David M. Patel For those eager to delve into the world of AI, particularly the buzz around generative AI, and seeking practical ways to harness tools like ChatGPT, MidJourney, or RunwayML for both business and personal advancement, this comprehensive guide is an invaluable resource. It begins with an exploration of AI’s history and its key components, delves into machine learning types, and discusses the crucial roles of data and algorithms. The guide further elucidates the major fields of AI, including NLP, computer vision, and robotics. In its deep dive into generative AI, it explains the concept, types, and offers business case studies, alongside a step-by-step approach to building and developing generative AI models. The final part focuses on practical applications in various fields like copywriting and graphic design, presenting the best AI tools of 2023 and addressing ethical considerations. Access the book here. Generative AI in Practice by Bernard Marr In Generative AI in Practice, renowned futurist Bernard Marr offers readers a deep dive into the captivating universe of GenAI. This comprehensive guide not only introduces the uninitiated to this groundbreaking technology but outlines the profound and unprecedented impact of GenAI on the fabric of business and society. It’s set to redefine all our jobs, revolutionize business operations, and question the very foundations of existing business models. Beyond merely altering, GenAI promises to elevate the products and services at the heart of enterprises and intricately weave itself into the tapestry of our daily lives. Access the book here. The Equalizing Quill by Angela E. Lauria As AI technology rapidly advances, AI-assisted book writing is becoming increasingly accessible to writers of all backgrounds. Learning how to unlock the potential of large language models is critical for communities who have been disenfranchised and are ready to make a bigger impact on society’s thinking. It is time to read The Equalizing Quill and finally make your voice heard. Access the book here.
Here AIM has listed the top seven must read generative AI books of 2023
Here AIM has listed the top seven must read generative AI books of 2023 for machine learning engineers and data scientists, enhancing your understanding and skills in the field of Generative AI.
["AI (Artificial Intelligence)", "Machine Learning"]
["GenAI", "artificial intelligence", "machine learning", "AI", "neural network", "ML", "Machine Learning", "computer vision", "NLP", "deep learning", "generative AI", "AI (Artificial Intelligence)"]
["AI", "artificial intelligence", "machine learning", "ML", "deep learning", "neural network", "NLP", "computer vision", "generative AI", "GenAI"]
Arya Vishwakarma is a journalism graduate who enjoys being in front of the camera and experimenting with many facets of the industry. She loves writing and anchoring and is an outgoing, gregarious person.