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Top Deep Learning Books to Read in 2021

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Deep learning training is an essential component of the broader subject training of AI and Machine Learning. Although it is still relatively new, its popularity is growing all the time, so it stands to reason that people would want to read and learn more about it.

Understanding Deep Learning is simple if you have a basic understanding of Machine Learning. Knowledge of Linear Algebra, Calculus, Probability, Programming Language, and Statistics is advantageous. We have compiled a list of books that will help you understand Deep Learning concepts and enrich your career as a deep learning expert.

  1. Advanced Deep Learning by Rowel Atienza

Advanced Deep Learning with Keras is a comprehensive guide to today’s advanced deep learning techniques, allowing you to create your own cutting-edge AI. You get to know practical project information that gives you equivalent practical experience and knowledge. It will be all regarding creating effective AI using the latest techniques using an open-source deep-learning library known as Keras. It provides an overview of MLPs, CNNs, and RNNs, which serve as the foundation for the book’s more advanced techniques.

This book begins with an explanation of how to implement deep learning models with Keras and Tensorflow and progresses to advanced techniques as you explore deep neural network architectures such as ResNet and DenseNet and how to create Autoencoders. 

You will then learn everything there is to know about Generative Adversarial Networks (GANs) and how they can unlock new levels of AI performance. Use variational AutoEncoders (VAEs) to see how GANs and VAEs can generate power for data synthesizing, conveniently convincing humans. 

Finally, you’ll learn how to use Deep Reinforcement Learning (DRL) techniques like Deep Q-Learning and Policy Gradient Methods, which have been critical to many recent AI results.

  1. Machine Intelligence by Suresh Samudrala

This book uses illustrations, data tables, and examples to explain the fundamental concepts of machine learning algorithms. You can learn topics such as neural learning, classical machine learning, and deep learning algorithms. It has a straightforward approach that builds from the ground up, which will benefit software engineers and students interested in learning about the field and those who may have begun without the benefit of a structured introduction or solid fundamentals.

The book is comprehensive without being overly mathematical. If one is looking for depth, the coverage of the subject is excellent, with most of the concepts required for understanding machine learning covered. This book is aimed at IT and business professionals who want to learn about these technologies but are put off by complex mathematical equations.

This book is also beneficial for students studying artificial intelligence and machine learning because it provides a conceptual understanding of the algorithms and an industry perspective. It provides a good overview for senior management.

  1. Deep Learning by Pearson Learn IT

Advance your career by taking self-paced online video courses. You can learn anywhere, at any time, and on any device. Pearson’s Learn IT courses can teach a lot in a short period, and the material is simple to understand and remember. It is a simple introduction to using Deep Learning models to process natural language data. Natural Language Processing Using Deep Learning Demonstrates concepts through real-world use cases and step-by-step, easy-to-follow exercises — video-based training by industry and academic experts with years of experience.

  1. Hands-On Deep Learning Algorithms by Sudharsan Ravichandran

You can easily understand the fundamentals and deep learning algorithms, including the mathematical principles, by explaining their practical applications. This book introduces you to deep learning algorithms, from basic to advanced, and shows you how to implement them from the ground up with TensorFlow. Throughout the book, you’ll learn about each algorithm, the mathematical principles that underpin it, and how to implement it as effectively as possible.

The book explains how to build neural networks, then moves on to TensorFlow, a powerful Python-based library for machine learning and deep learning. The book then delves into RNNs and LSTM and how to generate song lyrics with RNN. Following that, you will master the mathematics of convolutional and capsule networks widely used in image recognition tasks. Then you’ll learn how to use CBOW, skip-gram, and PV-DM to teach machines how to understand the semantics of words and documents. Following that, you investigate various GANs, such as InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE.

By the end of this book, you will have acquired all of the skills required to implement deep learning in your projects. If you are a data scientist, machine learning engineer, AI developer, or planning to gain more knowledge on deep learning and neural networks, this book is the perfect one for you to start studying. Those who are new to deep learning but have some experience with machine learning and Python programming will find the book extremely useful.


  • You will have a chance to learn to build neural networks from the beginning stage. 
  • Learn about the mathematical principles underlying deep learning algorithms.
  • TensorFlow can be used to implement popular deep learning algorithms such as CNNs, RNNs, and others.
  1. Deep Learning by Francois Chollet

It is designed for beginners and intermediate programmers. It goes over the implementation of a convolutional neural network in great detail. It is organized around a series of practical code examples that help to illustrate each new concept and best practices. 


Deep Learning training has attracted much attention and will continue to do so because it has many real-world applications. It enables us to fully utilize all labeled, unlabeled, structured, and unstructured data and provides enormous benefit for real-world applications. It is a powerful engine that generates actionable results. Deep Learning is here to stay for the foreseeable future, and a career in this field can help you reap significant personal and professional benefits in the long run.