7 Best Deep Learning Books of 2022 – DL (Ranks and Reviews)

deep learning books
Deep Learning (Image via google.com)

Want to get right to the best deep learning books? My favorite choices are Deep Learning with Python and Deep Learning: A Visual Approach.

A subset of machine learning is Deep Learning which refers to neural networks with multiple layers between input and output data layers, allowing the network to model high-level abstractions.

Deep learning is already transforming the world we live in, and it isn’t slowing down. In fact, according to the reports, the deep learning market is expected to be worth $18.16 billion by 2023.

Deep Learning is clearly one of the hottest topics in the artificial intelligence world, which is, fortunately, growing its market at a rocket speed. And, there’s no better way other than reading the best deep learning books to explore the machine learning bandwagon.

So, you want to read, learn, and explore the amazing tech world?

Welcome to the club of tech enthusiasts.

In today’s list, I set out to find the best deep learning books of the year to make it easier for you to learn about this exciting yet complex concept of DL.

Disclaimer: Please note, I may receive affiliate commissions from Amazon LLC if you decide to purchase books through checkout links available on this page. However, these commissions are at no extra cost to you and my goal is to give you the very best review and recommendations. Read my full disclaimer here.

Let’s get started.

What is Deep Learning (DL)?

Deep learning is a subset of machine learning that uses artificial neural networks with multiple hidden layers of units to learn representations of data. It is one of the most trending topics in today’s technology, and it will possibly play an even more important role in the coming year. 

Although deep learning has been mainly used for advanced applications such as image recognition, natural language processing, and autonomous vehicles, many users feel overwhelmed with its complicated mathematical concepts. 

The concept of deep learning has taken the world by storm. It is a concept that continues to be on the rise and has already made an impact worldwide. The demand for experts in this particular field is huge, so it’s no surprise that everyone is trying to learn as much as they can about deep learning.

Actually, Deep learning’s revolutionary approach has dramatically increased the field’s attention and popularity. The reasons behind this are obvious as it can help solve problems in many areas such as healthcare, big data, image recognition, natural language processing, machine translation, etc.

What are the Best Deep Learning Books?

Here are my top picks for the best deep learning books to read this year:

1. Deep Learning with Python: Second Edition.

This book is an invaluable resource for both newcomers to the field of machine learning and experienced practitioners who want to transition into the expanding field of deep learning.

You’ll find everything you need to get up to speed quickly and then explore in-depth topics such as neural networks, autoencoders, recurrent neural networks, LSTMs, and generative models.

You’ll explore the Keras library in-depth using real-world applications that demonstrate how to work with text, speech, time series, images, and shape data. Although deep learning is an active and fast-moving field, the core ideas have remained the same.

As you train more complex neural networks in your own work, remember to focus on building and improving your models rather than their input parameters. Keep the big picture in mind, and you’ll continue to be a successful deep-learning practitioner for years to come!

Throughout the book, you’ll also develop and refine a feedforward artificial neural network that classifies handwritten digits. This edition, it goes beyond simply training a standard neural network.

It’ll extend the model to run efficiently on multiple processor cores and across multiple machines with the-distributed so that you can train your network much faster than if you were working locally.

Additionally, it’ll cover recent developments such as Generative Adversarial Networks (GANs) and new techniques for training GANs using Theanotransfer learning. By the end of this book, you should have confidence in implementing your own deep-learning projects using Python.

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2. Deep Learning: A Visual Approach.

Deep Learning has been around for about two decades, but it’s only recently that it’s become part of the mainstream machine learning conversation.

This is because models have gotten large and fast enough to start pushing the boundaries of what machine learning can get done today. Neural networks are starting to rival many of the standard rule-based methods as well, and in some cases top them outright.

In fact, Deep Learning is making its way into medicine, self-driving cars, natural language processing, and much more. This book is designed to be very approachable; if you’re new to deep learning — or science or engineering in general — you should be able to handle it.

But if you do have experience with mathematics, this book will give you just the basic knowledge necessary to understand the fundamental concepts and get right to work applying them yourself.

Content is taught at a slow pace, but this allows you to retain information well so that you won’t find yourself continuously flipping back to the book. Overall, this book is a great beginner-level resource for anyone interested in the fascinating field of Deep Learning.

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3. Learning Deep Learning.

This book is aimed at readers with some experience in Python and an interest in Deep Learning.

Although no previous knowledge of DL is assumed, a basic programming background using Python as well as familiarity with important computer science topics including Control Flow, Regular Expressions, Functions, List Comprehensions, and Data Structures will be helpful.

While this book has plenty of practical examples to help ensure that you really understand the concepts being presented, it isn’t the best starting point for the novice to get up to speed in this exciting and fast-moving field.

In the end, if you want to learn about deep learning, this book has already proven to be a useful resource. If you’re looking for a way to get started with deep learning and neural networks, this book is an easy introduction to different concepts.

In the same way that people learned JavaScript back in the early days of web-based apps, Deep Learning will be a relevant resource for years to come as people start experimenting with AI.

Even if you are familiar with neural networks or machine learning, you will find value in this book. It gives a broad overview of deep learning including TensorFlow and Keras, which essentially serve as frameworks for doing deep learning.

It is also extremely useful for its coverage of concepts such as convolutions and activations, which come in handy when programming neural networks. Ultimate, it’s one of the great intermediate-level deep learning books.

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4. Math For Deep Learning.

Deep learning is largely about math, and there will always be new techniques to relate it back to when explaining it to laypeople.

Through this book, you’ll be able to learn more about how the deep learning models work from a mathematical perspective and plan out future deep learning projects using some of the techniques described in Math for Deep Learning.

I highly recommend this book if you want a greater understanding of DL fundamentals.

In “Math for Deep Learning”, the author provides a clear, step-by-step introduction to deep learning and machine learning. He also uses Python, NumPy, and Theano to solve equations and demonstrate concepts.

Yes, you will need to get through the math in order to be proficient at using mathematical algorithms, but there is no need to be intimidated.

You will find that it’s quite satisfying when you can figure out a hard concept on your own! And if you use this book as a guide, you’ll be well on your way to becoming proficient with mathematical algorithms that are used in the most cutting edge of machine learning and deep learning projects.

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5. Deep Learning For Coders with Fastai and PyTorch.

If you’re interested in learning more about deep learning, the latest AI technology that’s transforming the world around us, and how to apply it to your coding projects, then this book is a great place to start. 

The new deep learning library fastai was developed by Jeremy Howard after he started dabbling with artificial neural networks and seeing the power of their applications. If you’d like to learn how to develop advanced machine learning models and improve your coding skills, this is the book for you. 

Howard and Guo don’t just explain the concepts behind deep learning; they provide an all-encompassing view of the process of machine learning, including what works, what doesn’t work, and most importantly, how to do it yourself. 

And while fastai isn’t limited to Python, learning (and using) Python will certainly be a worthwhile investment, as it is a language used in data science and machine learning across industries — and not just in AI.

Deep Learning for Coders is an excellent reference point for anyone new to the field, and it provides a more extensive exploration of deep learning in action than any other similar resource.

If you love data, want to learn the fundamentals of deep learning, think that AI is important and you want to get a better grip on the topic, this book can greatly help you achieve your goal. 

It’s not an easy read and it may require multiple readings if you really want to capture all the knowledge and understanding. But it is still an enjoyable read.

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6. Deep Learning on Graphs.

Deep learning established a revolutionary approach to machine learning in general and more recently to graph neural networks.

The widespread application of deep learning techniques has revolutionized the fields of natural language processing, computer vision, data mining, biochemistry, and healthcare.

It is also one of the active research areas in both academia and industry; graph neural networks have been used in pharmaceuticals and even in the developing field of national defense.

This book differs from other graph-based books and it has four sections: General Concepts, Methods, and Applications of Graph Neural Networks, Related Models, and Future Research on Graph Neural Networks.

Although this book is centered on deep learning on graphs, it’s useful for those who want an overarching look at approaches to studying large-scale graphs.

They provide a new way to learn rich structured-data models from raw data. This book is a reference resource that we hope will enable this promising technology to mature into practical systems.

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7. Deep Learning Interviews.

Between online sources, books, lectures, and blogs, there’s a wealth of information out there for those who seek it and who can retain it. But the learning process is still often a difficult journey of small successes (and occasional failures).

The great thing about books like Deep Learning Interviews is that they offer a central place to learn from a host of different experts. These are people who have been in your position, who have found success and developed expertise.

Whether you’re looking to interview for grad school or a job in deep learning or AI, you’ll get benefitted from this book.

This book bridges the gap between theoretical AI and the application of these ideas in real-world systems. It creates a training ground for students and practicing artificial intelligence professionals.

If you’re brand new to deep learning, it is your ideal training manual. If you’re an experienced deep learning practitioner, it is your go-to resource for preparing for job interviews and exams.

No matter where you are in your academic journey, this book is a step in the right direction to reach your goals of becoming the best deep learning expert that you can be.

Overall, Deep Learning Interviews is an excellent resource for students preparing to enter the workforce. I strongly recommend checking it out if you have even a faint interest in AI or machine learning.

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Summary.

That’s it for my list of the best deep learning books.

Given the fact that there’s currently no dearth of deep learning textbooks, hopefully, this list and my reviews will make it easier for you to find the textbook that’s truly best for your needs.

Remember, just because a book is highly ranked on Amazon doesn’t mean it’s the absolute best for you. Take the time to peruse my list and read my reviews; it could open up a whole new understanding of DL for you.

The future of deep learning will be as bright as the books written by its pioneers. Deep learning has a huge potential to solve all sorts of problems, from searching for planet-like objects in the distant cosmos to designing autonomous vehicles that can negotiate high traffic intersections.

As DL continues to bend toward perfection, I hope that you’ll find these books helpful in your journey to explore the latest and the greatest innovations of the 21st century.

Here is a final summary of my top picks:

  1. Deep Learning with Python: Second Edition.
  2. Deep Learning: A Visual Approach.
  3. Learning Deep Learning.
  4. Math For Deep Learning.
  5. Deep Learning For Coders with Fastai and Pytorch.
  6. Deep Learning on Graphs.
  7. Deep Learning Interviews.

In the end, most of these books are very beginner-friendly, so don’t be intimidated! If you’re interested in getting started with deep learning and DL, then I recommend trying a few of these recommendations out. Happy reading!

What did you think of this list? Are there any books not mentioned? Let me know in the comment section.

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