Generative AI is gaining quite a bit of attention in 2023. With everyone busy experimenting with it and building innovative applications and tools for the betterment of humanity, it becomes increasingly important to understand the fundamentals and technical nuances rather than getting carried away by the hype.
Here, AIM lists the top 7 books on generative AI that machine learning engineers and data scientists must read in 2023 to enhance their understanding and skills in the field of generative AI.
Generative AI with Python and TensorFlow 2
by Joseph Babcock and Raghav Bali
This book, Generative AI with Python and TensorFlow 2 by Joseph Babcock and Raghav Bali, gives you a glimpse into the evolution of generative models from Boltzmann machines to VAEs and GANs, learns to implement TensorFlow models, and stays up to date on deep neural network research.
Access this book here.
Generative Deep Learning
by David Foster (author) & Karl Friston (preface)
Generative Deep Learning by David Foster and Karl Friston teaches machine learning engineers and data scientists how to create generative deep learning models using TensorFlow and Keras, including VAEs, GANs, Transformers, normalized flows, energy-based models, and denoising diffusion models. It covers deep learning fundamentals and advanced architectures, and offers tips for efficient learning and creativity.
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Generative AI with LangChain
by Ben O'Farth
Generative AI with LangChain by Ben Auffarth discusses the features, capabilities, and limitations of LLR models such as ChatGPT and Bard, and how to use the LangChain framework in 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 to deepen our understanding of the world.
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Generative AI on AWS
Chris Flegley, Antje Barth, Shelby Eigenbrode
You will learn about the life cycle of a generative AI project, including use case definition, model selection, model fine-tuning, search-enhanced generation, reinforcement learning from human feedback, model quantization, optimization, and deployment. You will also learn about different types of models, including Large Scale Language Models (LLMs) and multi-modal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images.
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Artificial Intelligence and Generative AI for Beginners
David M. Patel
This comprehensive guide is an invaluable resource for anyone looking to delve deeper into the world of AI, especially the buzz around generative AI, and for those looking for practical ways to leverage tools like ChatGPT, MidJourney, and RunwayML for both business and personal development. We begin by exploring the history and key components of AI, then delve into the types of machine learning and explain the crucial role of data and algorithms. Additionally, the guide delves into key areas of AI, including NLP, computer vision, and robotics. We dive deep into generative AI, explaining its concepts and types, providing business case studies, as well as showing a step-by-step approach to building and developing generative AI models. The final part focuses on practical applications in various fields such as copywriting and graphic design, introduces the best AI tools for 2023, and addresses ethical considerations.
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Generative AI in Action
Bernard Marr
in Generative AI in ActionIn GenAI, renowned futurist Bernard Marr offers readers a deep insight into the fascinating world of GenAI. This comprehensive guide not only introduces this groundbreaking technology to those new to it, but also outlines GenAI's profound and unprecedented impact on the fabric of business and society. GenAI will redefine all of our work, transform how businesses operate, and call into question the very foundations of existing business models. GenAI promises not just to change, but to elevate the core products and services of companies, becoming intricately woven into the tapestry of our daily lives.
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Equalizing Quill
Angela E. Lauria
As AI technology rapidly advances, AI-assisted book writing is becoming more and more accessible to writers of all backgrounds. Learning how to unlock the potential of large-scale language models is critical for disenfranchised communities and ready to have a profound impact on society's thinking. The time to read is now. Equalizing Quill And finally, have your voice heard.
Access this book here.
