The best 11 Deep Learning books

A Human Algorithm by Flynn Coleman explores the ethical and societal implications of artificial intelligence. It delves into the ways in which AI is shaping our world, from influencing our decisions to redefining what it means to be human. Coleman challenges us to consider the moral responsibilities that come with creating and using AI, and offers thought-provoking insights into how we can ensure a future where technology serves humanity.

Advances in Financial Machine Learning by Marcos Lopez de Prado explores the application of machine learning techniques in the financial industry. It delves into topics such as feature engineering, cross-validation, and algorithmic trading, providing valuable insights and practical guidance for professionals and researchers in the field.

Alexa For Dummies by Paul McFedries is a comprehensive guide to understanding and utilizing Amazon's virtual assistant, Alexa. Whether you're a beginner or an experienced user, this book provides step-by-step instructions, tips, and tricks to help you make the most of Alexa's capabilities. From setting up your device to using skills and creating routines, this book covers everything you need to know to become an Alexa expert.

Deep Learning from Scratch by Seth Weidman is a comprehensive guide that takes you through the fundamentals of deep learning. Starting from the basics of neural networks, the book provides a hands-on approach to building and training your own deep learning models from scratch. With clear explanations and code examples, it equips you with the knowledge and skills to understand and implement advanced deep learning concepts.

Deep Learning for Coders with Fastai and PyTorch is a practical book that introduces deep learning concepts in a hands-on manner. Written by Jeremy Howard and Sylvain Gugger, it aims to make deep learning accessible to coders and practitioners without a background in data science or machine learning. The book covers topics such as neural networks, convolutional neural networks, recurrent neural networks, and transfer learning, using the Fastai and PyTorch libraries.

Hands-On Deep Learning Algorithms with Python by Sudharsan Ravichandiran is a comprehensive guide that helps you master deep learning concepts and algorithms using Python. It provides practical examples and step-by-step instructions to build and train your own deep learning models. Whether you're a beginner or an experienced data scientist, this book will equip you with the knowledge and skills to tackle real-world deep learning challenges.

Introducing Artificial Intelligence by Henry Brighton is a comprehensive guide that demystifies the complex world of AI. It explores the history, current developments, and potential future of artificial intelligence, all presented in an accessible and engaging manner. Whether you're a tech enthusiast or a newcomer to the subject, this book provides a great foundation for understanding AI and its impact on our lives.

Machine Learning with Python Cookbook by Chris Albon is a comprehensive guide that provides practical solutions to real-world machine learning problems using Python. It covers a wide range of topics, from data preprocessing and feature engineering to model evaluation and deployment. With its hands-on approach and code examples, this book is a valuable resource for both beginners and experienced practitioners in the field of machine learning.

The Simulation Hypothesis by Rizwan Virk explores the idea that our reality may be a computer-generated simulation. Drawing on insights from diverse fields such as philosophy, science, and technology, the book delves into the implications of this thought-provoking hypothesis and its potential impact on our understanding of the universe.

'Make Your Own Neural Network' by Tariq Rashid is a practical guide that helps readers understand the concepts of neural networks and how to build one from scratch. With clear explanations and step-by-step instructions, the book provides a hands-on approach to learning about this fascinating area of technology. Whether you're a beginner or have some experience in programming, this book can help you dive into the world of neural networks.

Our Final Invention by James Barrat delves into the potential dangers of artificial intelligence (AI) and the race to create superintelligent machines. Barrat explores the ethical and existential implications of AI, and raises thought-provoking questions about the future of humanity in a world where machines may surpass human intelligence.