Generative AI has become a significant topic of interest in 2023. While many are busy exploring its potential and creating innovative applications, it is crucial to understand the fundamentals and technical nuances to avoid falling prey to the hype.
To help machine learning engineers and data scientists enhance their understanding and skills in the field of Generative AI, AIM has compiled a list of the top seven must-read books for 2023.
Modern Times Series Forecasting with Python by Manu Joseph offers a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems using machine learning and deep learning techniques. It covers essential topics such as global forecasting models, cross-validation strategies, and forecast metrics. By the end of the book, readers will have the tools to build world-class time series forecasting systems and tackle real-world problems.
Generative AI with Python and TensorFlow 2 by Joseph Babcock and Raghav Bali provides insight into the evolution of generative models, from Boltzmann machines to VAEs and GANs. Readers will learn TensorFlow model implementation and stay up-to-date with deep neural network research.
Generative Deep Learning by David Foster and Karl Friston educates machine learning engineers and data scientists on creating generative deep learning models using TensorFlow and Keras. The book covers a wide range of topics, including VAEs, GANs, Transformers, normalizing flows, energy-based models, and denoising diffusion models. It not only covers the basics but also delves into advanced architectures, providing valuable tips for efficient learning and creativity.
Designing Machine Learning Systems by Chip Huyen takes a holistic approach to designing reliable, scalable, maintainable, and adaptive ML systems. The book equips readers with the skills necessary to navigate changing environments and meet evolving business requirements.
Interpretable Machine Learning with Python, Second Edition by Serg Masis teaches the key concepts of interpreting machine learning models using real-world data. The book covers various techniques, from traditional methods like feature importance and partial dependence plots to integrated gradients and gradient-based attribution methods. It provides hands-on techniques for tuning models, reducing complexity, mitigating bias, and enhancing reliability.
Generative AI with LangChain by Ben Auffarth explores the functions, capabilities, and limitations of LLR models such as ChatGPT and Bard. It guides readers on using the LangChain framework for production-ready applications, covering transformer models, attention mechanisms, training and fine-tuning, data-driven decision-making, and automated analysis and visualization. The goal is to provide a comprehensive understanding of LLMs and their potential for enhancing our understanding of the world.
These seven must-read generative AI books of 2023 offer valuable insights and skills to those interested in the field. By delving into these resources, machine learning engineers and data scientists can enhance their technology prowess and unlock new possibilities for the betterment of humanity.
With generative AI becoming increasingly prominent, acquiring in-depth knowledge and upskilling is essential to stay ahead of the curve. These recommended books not only provide technical know-how but also offer practical guidance for building innovative applications and tools.
