10 Best Deep Learning Books for You in 2022

In an age when the importance of data has become immense, deep learning is becoming increasingly popular. When working with large amounts of data, deep learning’s capacity to handle vast amounts of features makes it incredibly robust and powerful.

Deep learning approaches, on the other side, maybe overkill for smaller jobs because they require enormous amounts of data to be helpful.

When applied to data science, deep learning can provide stronger and more appropriate processing algorithms. Its capacity to learn without supervision allows it to increase accuracy and results over time. It also provides more accurate and succinct analytical findings to data scientists.

Here are the 10 best deep learning books to help you understand it better.

Best Deep Learning Books for You in 2022

1. Deep Learning Yoshua Bengio

Deep Learning (Adaptive Computation and Machine Learning series)

Author: Yoshua Bengio
Publisher: MIT
Edition: Illustrated edition
Available in: Kindle, Paperback

About the book

The topics covered in this book cover a wide spectrum of deep learning issues.

The book lays a conceptual and mathematical foundation by addressing subjects including numerical computation, probability theory, linear algebra, information theory, and machine learning. Object tracking, natural language, online recommender systems, speech recognition, bioinformatics, and video games are among the applications covered.

It also covers deep learning techniques used by practitioners in the industry, such as normalization, and optimization algorithms, deep feedforward networks, sequence modeling, convolutional systems, and practical methodology.

Finally, the book includes research perspectives on Monte Carlo methods, linear factor models, representation learning, approximation inference, autoencoders, the partition function, structured probabilistic models, and deep generative models, among other theoretical subjects.

You can buy this book from here.

2. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems

Author: Audrelian Geron
Publisher: O’Reilly
Edition: 2nd
Available in: Kindle, Paperback

About the book

Deep learning has recently enhanced the entire area of machine learning thanks to a succession of discoveries. Even programmers with little experience in this field can now utilize simple, fast tools to create data-driven programs. This easy-to-understand guide will explain to you how to do it.

Author Aurélien Géron helps you obtain an intuitive grasp of the principles and tools for building intelligent systems by using simple examples, little theory, and two output-ready Python frameworks—Scikit-Learn and Tensor Flow. From simple linear regression to deep learning models, you’ll study a variety of strategies.

To get started, you only need programming skills and the activities in each section to help you implement what you’ve studied.

What you’ll learn

  • Investigate the landscape of machine learning, particularly neural networks.
  • Track an example machine-learning project from start to finish with Scikit-Learn.
  • Investigate various training models, such as support vector machines, random forests, decision trees, and group approaches.
  • To create and train neural networks, use the Tensor Flow library.
  • Investigate neural net designs such as recurrent nets, convolutional networks, and deep reinforcement learning
  • Learn how to train and scale deep neural networks

You can buy this book from here.

3. Deep Learning with Python

Deep Learning with Python, Second Edition

Author: Francois Chollet
Publisher: Manning
Edition: 2nd
Available in: Kindle, Paperback

About the book

This fully rewritten and updated second edition teaches deep learning with Python and Keras and is jam-packed with useful information for both new and seasoned machine learning practitioners. You’ll discover useful approaches for applying neural networks in the real world, as well as important theories for refining them.

The second edition of Deep Learning with Python explains the field of deep learning using Python and the powerful Keras framework.

François Chollet, the creator of Keras, gives advice to both novice and expert machine learning practitioners in this revised and enlarged new edition. As you read this book, you’ll gain a better knowledge of the subject through simple explanations, bright color images, and straightforward examples. You’ll quickly develop the skills required to begin constructing deep-learning apps.

What you’ll learn

  • What exactly is deep learning?
  • Neural network mathematical building blocks
  • Keras with TensorFlow Overview
  • Introduction to Neural Networks: Classification and Regression
  • Machine learning basics
  • Machine learning’s generic workflow
  • Working with Keras: An In-Depth Look
  • Deep learning for computer vision: an overview
  • Deep learning for computer vision at its most advanced
  • Time series deep learning

You can buy this book from here.

4. Deep Learning for Coders with Fastai and PyTorch

Deep Learning for Coders with Fastai and PyTorch AI Applications Without a PhD

Author: Jeremy Howard
Publisher: O’Reilly
Edition: 1st
Available in: Kindle, Paperback

About the book

Deep learning is frequently assumed to be the exclusive realm of math PhDs and huge tech corporations. However, as demonstrated in this hands-on approach, Python developers with no math background, small amounts of data, and minimal code may get great deep learning results.

How?

Fastai is the first library to provide a uniform interface for the most popular deep learning apps.

The creators of fastai, Jeremy Howard and Sylvain Gugger demonstrate how to use fastai with PyTorch to train a model for a variety of applications. You’ll also delve deeper into deep learning theory to fully comprehend the algorithms at work.

You can buy this book from here.

5. The Hundred-Page Machine Learning Book

The Hundred-Page Machine Learning Book

Author: Andriy Burkov
Publisher: Andriy Burkov
Edition: 1st
Available in: Kindle, Paperback

About the book

Unlike Chollet Deep Learning with Python or Gulli’s Keras or Tensorflow, this book focuses on discussing concepts rather than offering code, which makes it a useful supplement to previous works.

This is not to say that this book is useless for those who are only interested in coding models; on the contrary, it delivers exactly what it promises in the title: a great introduction to machine learning, and we believe that this makes it the go-to handbook for managers or software developers looking for a single volume on machine learning.

You can buy this book from here.

6. Grokking Deep Learning

Grokking Deep Learning

Author: Andrew Trasking
Publisher: Manning
Edition: 1st
Available in: Kindle, Paperback

About the book

Understanding Deep Learning teaches you how to build deep learning neural networks from the bottom up! Andrew Trask, a professional deep learning specialist, explains the theory underlying neural network programming so you may comprehend every aspect for yourself.

Using only Python and its arithmetic module, NumPy, you’ll train your own neural networks to sense and understand images, convert text into several languages, and even write like Shakespeare. After completing this course, you will be fully prepared to go on to master deep learning.

What you’ll learn

  • How neural networks ‘learn’
  • You will build neural networks that can see and understand images
  • You will build neural networks that can translate text between languages and even write like Shakespeare
  • You will build neural networks that can learn how to play videogames

You can buy this book from here.

7. Deep Learning: A Visual Approach

Deep Learning A Visual Approach

Author: Andrew Glassner
Publisher: O’Reilly
Edition: Illustrated edition
Available in: Kindle, Paperback

About the book

Deep Learning: A Visual Approach is for anybody who wants to learn more about this fascinating area without having to rely on complex math and coding. If you want to understand how these tools function and how to utilize them, the answers are there on these pages. If you’re ready to start writing your own applications, there are plenty of extra Python notes in the associated Github repository to get you started.

The book’s conversational tone, abundant color pictures, fascinating analogies, and real scenarios brilliantly illustrate the major ideas of deep learning.

You can buy this book from here.

8. Deep Learning: A Practitioner’s Approach

Deep Learning A Practitioner's Approach

Author: Josh Patterson
Publisher: O’Reilly
Edition: 1st
Available in: Kindle, Paperback

About the book

Despite the fact that interest in machine learning is at an all-time high, unrealistic expectations frequently sabotage projects before they even get off the ground. How might machine learning, particularly deep neural networks, help your company succeed? This practical guide not only gives you the most up-to-date information on the subject, but it also shows you how to get started developing effective deep learning networks.

Before providing their open-source Deeplearning4j (DL4J) toolkit for constructing production-class workflows, authors Adam Gibson and Josh Patterson provide theory on deep learning. You’ll cover methods and tactics for training deep network architectures and performing deep learning workflows on Spark and Hadoop with DL4J using real-world applications.

What you’ll learn

  • Dive into broad machine learning principles as well as deep learning in particular.
  • Discover how deep networks evolved from neural network basics.
  • Investigate the key deep network designs, such as Convolutional and Recurrent.
  • Learn how to apply specific deep networks to the appropriate situation.
  • Take a look at the basics of adjusting generic neural networks and specialized deep network topologies.
  • Use DataVec, DL4J’s workflow tool, to apply vectorization algorithms to various data formats.

You can buy this book from here.

9. Artificial Intelligence: What You Need to Know About Machine Learning, Robotics, Deep Learning, Recommender Systems, Internet of Things, Neural Networks, Reinforcement Learning, and Our Future

Artificial Intelligence What You Need to Know About Machine Learning, Robotics, Deep Learning, Recommender Systems, Internet of Things, Neural Networks, Reinforcement Learning, and Our Future

Author: Neil Wilkins
Publisher: Bravex publications
Edition: 1st
Available in: Kindle, Paperback

About the book

Are you interested in learning more about artificial intelligence but are unsure what it is all about?

Machine learning, robots, and the internet of things are all covered in this book.

You may use it as a handy reference tool anytime you see news headlines about Google or Facebook making a new AI achievement.

You’ll know what deep neural networks are, how stochastic gradient and backpropagation function, and what deep learning is by the end of this book. You’ll also get a detailed overview of AI’s history, from the first automation in yesteryear to today’s driverless automobiles.

  • Learn how machines “think” and how they learn.
  • Discover the five reasons why experts are concerned about AI research.
  • Discover the truth behind the top six artificial intelligence misconceptions.
  • Learn about neural networks and how they function as the “brains” of machine learning.
  • Learn about reinforcement learning and how it may be used to train machine learning systems through experience.
  • Learn about the most recent state-of-the-art artificial intelligence approaches that employ deep learning.
  • Discover the fundamentals of recommender systems.
  • Extend your present understanding of machinery and what contemporary robots can do.

You can buy this book from here.

10. Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence

Deep Learning Illustrated A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & Analytics Series)

Author: John Krohn
Publisher: Addison-Wesley Professional
Edition: 1st
Available in: Kindle, Paperback

About the book

Deep Learning Illustrated is a visually appealing, intuitive, and an understandable high-level overview of deep learning applications and principles written by three world-class professors and practitioners. Because of its wealth of vibrant, full-color images, it isolates many of the complexity of constructing deep learning models, making the area more pleasurable to study and access to a far broader audience.

Part I provides a high-level introduction of Deep Learning, explaining what it is, why it has gained popularity, and how it relates to ideas and terminology such as AI, Machine Learning, Artificial Neural Networks, and Reinforcement Learning.

The first few chapters are jam-packed with colorful graphics, simple analogies, and character-driven stories.

The authors then provide a practical reference and tutorial for using a wide range of established deep learning techniques, building on this basis. Essential theory is presented with as little mathematics as feasible and is supplemented with practical Python code. All significant deep learning strategies and their applications, including natural language processing, machine vision, image generation, and video gaming, are supported by practical “run-throughs”.

You can buy this book from here.

Conclusion

Hopefully, this list of the best deep learning books will help you learn everything about it and build yourself a shining career. It doesn’t matter which of these books you pick, each one is packed with information and knowledge. So, which book are you picking first? Let us know via comments.

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