Top 5 Books on AI and ML to Grab Today

It has been popularly noted that artificial intelligence would be like the ultimate version of Google. With recent advancements in research and technology, Artificial Intelligence (AI) and Machine Learning (ML) are slowly becoming a part of our routine.

The pace at which technology is growing is unfathomable. As these smart technologies engulf our life, staying updated with them is the need of the day. So, here’s Packt’s selection of finest books in artificial intelligence and machine learning that will help you have an edge in these fields:

Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran

Reinforcement Learning is the trending and one of the most promising branches of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. It is a hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python.

Generative Adversarial Networks Cookbook by Josh Kalin

Now, simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow, and Keras. Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find a code that is easy to understand. This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation.

Hands-On Machine Learning for Cybersecurity by Soma Halder and Sinan Ozdemir

Cyber threats today are one of the costliest losses that an organization can face. Get into the world of smart data security using machine learning algorithms and Python libraries. This book will show you the most efficient tools to solve the big problems that exist in the cybersecurity domain.

Hands-On Transfer Learning with Python by Dipanjan Sarkar, Raghav Bali, and Tamoghna Ghosh

Transfer learning is a machine learning technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.

Hands-On Meta Learning with Python by Sudharsan Ravichandiran

Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning, you can learn from small datasets faster. Explore a diverse set of meta-learning algorithms and techniques to enable human-like cognition for your machine learning models using various Python frameworks with Hands-On Meta Learning with Python.


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