15 Best Deep Learning Books for Beginner

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville This book is often considered the "Bible" of deep learning. It covers a wide range of topics, from the basics to the latest research, in a comprehensive yet accessible manner.

"Neural Networks and Deep Learning: A Textbook" by Charu C. Aggarwal This book provides a solid foundation in neural networks and deep learning, with clear explanations and practical examples.

"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron While not exclusively focused on deep learning, this book is an excellent hands-on guide that covers deep learning concepts using popular libraries like TensorFlow and Keras.

"Deep Learning for Computer Vision" by Rajalingappaa Shanmugamani Specifically geared towards computer vision, this book explains how to use deep learning techniques for tasks such as image classification, object detection, and image generation.

"Python Deep Learning" by Ivan Vasilev and Daniel Slater A practical guide to implementing deep learning models in Python, this book covers popular libraries such as TensorFlow, Keras, and PyTorch.

"Grokking Deep Learning" by Andrew W. Trask Aimed at beginners, this book uses intuitive explanations and simple examples to help you understand the core concepts of deep learning.

"Deep Learning Illustrated" by Jon Krohn This book takes a visual and intuitive approach to explaining deep learning concepts, making it accessible to beginners.

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville This book is often considered the "Bible" of deep learning. It covers a wide range of topics, from the basics to the latest research, in a comprehensive yet accessible manner.

"Deep Learning from Scratch: Building with Python from First Principles" by Seth Weidman As the title suggests, this book teaches you how to build deep learning models from scratch using Python, without relying on pre-built libraries.

"Deep Learning with PyTorch" by Eli Stevens, Luca Antiga, and Thomas Viehmann PyTorch is a popular deep learning framework, and this book provides a hands-on introduction to using it for building and training neural networks.

"Deep Learning: A Practitioner's Approach" by Adam Gibson and Josh Patterson This book offers a practical approach to deep learning, focusing on the tools and techniques needed to build and deploy deep learning models.

"Deep Learning for Natural Language Processing" by Palash Goyal, Sumit Pandey, and Karan Jain If you're interested in using deep learning for tasks like text classification, sentiment analysis, and machine translation, this book is a great starting point.

"Practical Convolutional Neural Networks" by Mohit Sewak, Md. Rezaul Karim, and Pradeep Pujari Convolutional Neural Networks (CNNs) are essential for tasks such as image recognition. This book provides a hands-on guide to building and training CNNs.

"Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play" by David Foster Explore the fascinating world of generative models with this book, which covers techniques for creating art, music, and more using deep learning.

"Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence" by Sandro Skansi This book offers a unique perspective by tracing the historical development of deep learning and explaining its core concepts through the lens of logical calculus.

"Machine Learning Yearning" by Andrew Ng Although not a deep learning book per se, this practical guide by one of the pioneers in the field, Andrew Ng, provides invaluable insights into how to structure machine learning projects, including those involving deep learning.