best deep learning books

Best Deep Learning Books: Reviews and Guide

Deep learning can seem daunting, especially without a proper guide. As one of the most rapidly evolving fields in artificial intelligence, it’s essential to have reliable resources.

This guide provides a concise review of the best deep learning books, helping you make an informed decision based on your current knowledge level and learning objectives. 

From beginners to advanced learners, these books offer insightful and up-to-date perspectives on deep learning concepts and applications.

Reviewing the Top 5 Best Deep Learning Books

The top five best deep learning books stand out for their comprehensive coverage, clear explanations, and basic concepts.

1. Deep Learning (The MIT Press Essential Knowledge Series)

Deep Learning (The MIT Press Essential Knowledge series) is an introductory guide that demystifies deep learning technology, its applications, and the principles underlying it. It connects abstract concepts to real-world applications in sectors such as driverless cars, AI games, and machine translation.

The book has distilled complex, deep-learning architectures and algorithms into comprehensible information. It thoroughly covers gradient descent, backpropagation, and capsule networks.

However, the book leans more towards the theoretical and conceptual side of deep learning rather than hands-on programming.

You don’t need to be a math genius to get it – a basic understanding of linear algebra and calculus will do. It’s handy for anyone starting in software engineering or working in a tech role.

What We Like

  • Offers an in-depth exploration of the mathematical underpinnings of DNNs
  • Provides a unique geometric approach to explaining deep learning
  • Highlights the transition from outdated to contemporary practices, like Sigmoid activation to LSTM with ReLu

Deep Learning is particularly suitable for newcomers who want to understand the AI technology that powers various cutting-edge applications.

If you want to stay informed about current trends and future directions of AI, you will find this book deeply enriching.

2. Deep Learning (Adaptive Computation and Machine Learning series)

If you’re a student, software engineer, or someone interested in AI, Deep Learning from the Adaptive Computation and Machine Learning series is highly beneficial. This book gives you a thorough understanding of the mathematical and conceptual underpinnings and dives into practical industry-used techniques.

It’s perfect for researchers and students who want to understand the science beneath the surface. The content is well-structured and brimming with mathematical explanations, making it a valuable reference if you have a strong mathematical inclination.

It also offers extensive insights into various deep-learning papers, adding depth and diversity to the learning experience. While its primary focus is on theory, this book is an excellent stepping-stone for exploring deep learning from a rigorous and scientific perspective. 

What We Like

  • Has an in-depth exploration of the theories and mathematics that form the basis of deep learning
  • Provides broad coverage on various aspects of deep learning
  • Offers clear and relevant examples to help illustrate complex concepts

If you’re interested in the “why” behind deep learning and aren’t deterred by the mathematical intricacies, this book would be an enriching addition to your library.

3. Teaching for Deeper Learning

Teaching for Deeper Learning equips educators with the tools needed to foster a more profound understanding in students. Jay McTighe and Harvey F. Silver leverage their extensive experience to present seven higher-order thinking skills, promising to promote greater knowledge retention and transfer.

The strategies and methodologies provided within the book are versatile and applicable across different content areas and grade levels.

One of the key strengths of this book is its practicality. It provides educators with the concepts and actionable strategies and tools to facilitate deeper learning.

The proposed teaching techniques are tailored to align with current academic standards, offering a comprehensive guide relevant to today’s educational landscape.

However, the book may come across as overly theoretical for some people, so it may get tricky to implement the presented strategies. 

What We Like

  • Covers higher-order thinking skills and methodologies in detail
  • Provides practical tools and strategies for classroom implementation
  • Aligns with current academic standards

Teaching for Deeper Learning is excellent for teachers who want to enhance their teaching methods and foster a deeper understanding among their students. 

4. Deep Learning With Python

Deep Learning with Python, Second Edition by François Chollet, a software engineer at Google and creator of the Keras library, provides a profound introduction to deep learning. Notably, this second edition has been extensively revised, rendering the book highly up-to-date with the current trends in the field.

Chollet ensures his guide is accessible to people relatively new to the field. The book introduces deep learning from the first principles gradually building up in complexity. So it’s ideal if you have intermediate Python skills, regardless of your background in machine learning.

Moreover, the author’s intimate familiarity with the field provides a unique advantage, as he shares insights that might not be available in other resources. These insider perspectives are found throughout the book, offering readers insightful understandings of the technology.

However, despite these strengths, the book might not help if you want a thorough dive into the mathematical nuances of deep learning algorithms.

What We Like

  • Covers essential deep learning topics extensively
  • Easily understandable by readers with intermediate Python skills
  • Offers practical techniques and real-world examples

Deep Learning with Python, Second Edition is an invaluable guide for Python coders looking to refine their artificial intelligence skills. 

5. Machine Learning With PyTorch and Scikit-Learn

Machine Learning with PyTorch and Scikit-Learn is a thorough and well-rounded exploration of the subject matter. It offers a detailed understanding of machine learning and deep learning principles while encouraging the practical application of these principles through Python libraries, PyTorch, and Scikit-Learn.

This book goes beyond merely guiding the reader on what to do. It delves into the why and how to empower learners to apply this knowledge to new problems and scenarios. Plus, it covers multiple essential techniques and introduces the latest trends, such as graph neural networks and transformers for natural language processing (NLP).

However, the book does assume a certain level of prerequisite knowledge. So a good understanding of calculus and linear algebra is necessary.

What We Like

  • Comprehensive coverage of machine learning techniques and concepts
  • Updated to include recent developments in the field
  • Insightful coverage of cutting-edge trends in the field
  • Practical hands-on approach supported by a robust GitHub repository

Machine Learning with PyTorch and Scikit-Learn” is great if you have a firm grip on Python basics. But it’s not an easy-breezy read and requires commitment.

How to Choose the Right Deep Learning Book?

Deep learning, a subfield of machine learning, has been pivotal in shaping the world we live in today. From speech recognition to autonomous driving, its applications are wide-ranging. Therefore, selecting a suitable deep learning book can be complex, as everyone has a different learning need and style.

This guide provides a comprehensive, fair, and objective framework for selecting the best deep-learning books for your specific requirements.

Background Knowledge

Your understanding of programming and machine learning principles plays a big role in choosing the right book. Understand the book’s content and determine whether it matches your knowledge level and learning goals.

You should be able to gain the intended knowledge without feeling overwhelmed or under-challenged.

For beginners with basic knowledge of Python and math, “Deep Learning for Python” by Francois Chollet can be a great start.

On the other hand, if you already have a solid foundation in machine learning, “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville can provide more in-depth knowledge.

Theoretical vs. Practical Approach

Deep learning books vary in their approach. Some place a greater emphasis on theoretical aspects, such as mathematical algorithms and conceptual frameworks, while others are more practically oriented, focusing on coding and implementation.

If you’re new to the field, a book with more practical examples can be more beneficial. However, a more theoretical approach might be needed if you want to deepen your understanding of the underlying principles.

“Teaching for Deeper Learning” focuses on the pedagogical aspect, helping readers understand how to structure their learning process. It’s a useful book for teachers or if you are self-taught and want to learn how to design your deep learning curriculum.

Application Area

Deep learning can be applied to numerous domains, from computer vision to natural language processing. Depending on your area of interest or the problem you’re trying to solve, the choice of book can change. Make sure the book you choose focuses on your area of interest.

If you’re more interested in specific deep learning applications, “Machine Learning with PyTorch and Scikit-Learn” offers a good balance of topics.

However, if you want a more comprehensive understanding of deep learning as a whole, you might want to go with “Deep Learning (Adaptive Computation and Machine Learning series).”

Learning Style

Different books follow different teaching methods – some use a step-by-step approach, some prefer to teach by giving real-world examples, and others might follow a more traditional academic style.

Evaluate the book based on your learning style for maximum comprehension.

Quality of Writing 

Clarity and simplicity of language matter. Technical topics can be hard to understand if not well explained.

A quick read of sample chapters or online reviews can give you an idea of the writing quality.

Coding Proficiency

A good deep-learning book should contain real code examples. It’s always beneficial to see the implementation of theories and algorithms in practice. Code examples also allow you to practice and better understand the material.

Your choice between “Deep Learning with Python” and “Machine Learning with PyTorch and Scikit-Learn” may depend on your comfort level with Python and your preference for specific deep learning libraries. The former uses Keras, a user-friendly neural network library, whereas the latter teaches you how to use PyTorch and Scikit-Learn, which are more flexible and have a steeper learning curve.

Updates and Supplements

As deep learning is rapidly evolving, the book you choose should be up-to-date. Some books come with helpful supplementary materials like exercises, online tutorials, and datasets for practice.

Peer Reviews

Look for books with positive reviews from other readers, especially those with a similar learning context. Detailed reviews can often give you a good sense of the strengths and weaknesses of the book.

Author’s Expertise

The author’s expertise and reputation in the field can also be an important factor. Authors with academic and industry backgrounds can provide a balanced perspective on theoretical concepts and their practical applications.

Book’s Format 

Depending on your preference, you should consider if the book is available in the format you want – hardcover, paperback, e-book, or even as an audiobook.

Visual Aids

Diagrams, flowcharts, and other visual aids can improve comprehension of complex concepts. If you’re a visual learner, this point is important to consider.

Length and Depth of Content 

Some readers may prefer detailed explorations of each topic. Look at the table of contents, the number of pages, and read some excerpts to get an idea of how in-depth the book goes into each topic.

Cost Value 

Lastly, consider if the price of the book is worth the value it provides. Some books might be expensive but offer comprehensive, up-to-date content with additional resources, making them worth the cost.


Is deep learning easy?

The complexity of understanding deep learning can be subjective and largely depends on your background. If you’re familiar with the basics of programming and have a good understanding of mathematics (linear algebra, calculus, and probability), you might find it easier to grasp the concepts of deep learning.

However, deep learning is a sophisticated field that involves understanding high-level mathematical concepts and algorithms, which can be challenging. 

Can I learn AI on my own?

Yes, you can learn AI on your own. Many platforms offer free or relatively affordable courses on various aspects of AI, including machine learning, deep learning, natural language processing, and more. However, learning AI requires dedication and commitment as the field is vast and complex.

Best Deep Learning Books Authors

Only a handful of authors have truly excelled in conveying the complexities of deep learning concepts with clarity and depth. Here are our top picks:

  • John D. Kelleher is an academic leader and author of several highly regarded deep learning and AI books. His accessible writing style and clear explanations have made him a respected figure in the field.
  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville are among the most reputable figures in deep learning. Goodfellow is known for inventing generative adversarial networks (GANs), while Bengio and Courville are celebrated for their work in machine learning and AI.
  • Jay McTighe and Harvey F. Silver are renowned education authors best known for their work on learning models and teaching strategies.
  • François Chollet is a renowned AI researcher at Google and the creator of the Keras deep learning library. His book, “Deep Learning with Python,” is highly praised for its lucid explanations and practical approach.
  • Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili are well-known authors in the AI field. Their joint work on Python Machine Learning is among the most popular books for learners in the field.

Final Words

Selecting the best deep-learning books is a highly personalized decision. It’s contingent on factors like your skill level, learning style, practical vs. theoretical inclination, and intended use.

Remember, the goal is to foster a deep understanding of the subject. You might not find the perfect book on the first try, and that’s fine.

The journey to learning deep learning is iterative, and every book you explore contributes to your growing knowledge pool.

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