10 Best Deep Learning Books for Learners of All Levels

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10 Best Deep Learning Books for Learners of All Levels

Ramya Shankar
Last updated on July 13, 2024

    Today’s cutting-edge technologies like deep learning, artificial intelligence, and machine learning significantly impact our everyday lives. Books are a great source for learning anything.

    These technologies have made human life more convenient than ever before, and they have made a massive contribution to automating the world. Though deep learning is relatively a new technology, its popularity is rising progressively.

    Hence, it makes sense that graduates and professionals in the IT industry want to know more about this technology. This article will first discuss deep learning and then list down the 10 most popular books that can help you build the deep learning skills required for building intelligent systems.

    What is Deep Learning?

    Deep learning is a subset of artificial intelligence (AI) and machine learning (ML) that emulates the human brain, allowing the system to gather data and make predictions with exceptional accuracy. DL neural networks have three or more layers.

    Also, these neural networks are responsible for mimicking the human brain and learning patterns from large sets of data. A single neural network layer can make a close prediction from data sets, but additional layers help achieve incredible accuracy. Moreover, deep learning is a crucial aspect of data science .

    For data scientists who need to collect, analyze, and interpret large sets of data, deep learning helps them perform such tasks quickly. Let us take one example of a toddler trying to identify a dog. The parents first help the toddler to identify a ‘dog’ by explaining how it looks. Later, the toddler learns the barking sound of the dog.

    Therefore, with specific characteristics, like appearance and the barking sound, a toddler can identify a dog. For a computer to recognize a dog, a programmer has to train it with features of a dog, i.e., define a set of data that recognizes a dog. A computer program with deep learning responds immediately with the utmost accuracy by using the data set provided.

    10 Best Deep Learning Books for Freshers and Experienced

    Mastering deep learning requires a strong understanding of machine learning and mathematical concepts like calculus, algebra, statistics, and probability. Here is a curated list of the ten best deep learning books that offer an exceptional learning experience.

    1. Deep Learning for Natural Language Processing

    Deep Learning for Natural Language Processing

    Author: Stephan Raaijmakers

    This book is ideal for readers having a basic understanding of Natural Language Processing (NLP) and intermediate Python expertise. However, readers do not need to hold any prior experience working with Keras or other deep learning toolkits.

    As its name suggests, this book helps you to interpret and utilize text efficiently by applying cutting-edge deep learning techniques to natural language processing tasks. Also, it will introduce you to critical NLP topics, like parsing, autoencoders, part-of-speech tagging, and neural word embedding.

    After introducing the key NLP topics, this book will guide you to deep dive into the advanced-level concepts, including hyperparameters, memory-based NLP, and linguistic structure for deep NLP.

    The author has incorporated several NLP examples to help you understand all the concepts effectively. You can buy the Paperback edition of the book that gives you access to the free ePub, Kindle, and PDF versions of the book.

    About the Author

    Stephan Raaijmakers is a senior scientist at TNO and a Communicative AI Professor at Leiden University.

    Publisher: Manning Publications
    Paperback Print Length: 404 pages

    You can buy this book from here .

    2. Advanced Deep Learning with Keras

    Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

    Author: Rowel Atienza

    The next name on our list of the best deep learning books is Advanced Deep Learning with Keras. This book covers advanced-level deep learning techniques that you can leverage to develop state-of-the-art intelligent systems.

    For understanding the content of this book effectively, you should possess a basic understanding of the Python language. It is one of the finest deep learning books that teach various DL techniques, like autoencoders, deep reinforcement learning, VAEs, and GANs. Moreover, this book helps you get acquainted with industry-recognized machine learning approaches.

    Initially, the author introduced the concepts that form building blocks for advanced deep learning techniques. These concepts include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multi-layer Perceptron (MLP). Later, the author covers topics that are essential to learning TensorFlow and Keras. Additionally, the book explains neural network architectures, like DenseNet and ResNet, and an approach to creating Autoencoders.

    Also, you will be able to understand Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs) along with the implementation of Deep Reinforcement Learning (DRL). You can get this book in Paperback and Kindle formats.

    About the Author

    Rowel Atienza is an Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines.

    Publisher: Ingram Short Title
    Paperback Print Length: 368 pages

    You can buy this book from here .

    3. Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow

    Hands-On Machine Learning with Scikit-Learn, Keras and Tensor Flow: Concepts, Tools and Techniques to Build Intelligent Systems (Colour Edition)

    Author: Aurelien Geron This is among the best deep learning books that introduce you to Keras, TensorFlow, and Scikit-Learn libraries. The author has provided a detailed explanation of various tools and deep learning concepts required to build intelligent systems. Each chapter of the book contains an exciting exercise, helping you apply your knowledge. The only requirement to start reading this book is to have Python programming experience. This book helps you to:

    • Understand the machine learning landscape, like neural nets.
    • Get acquainted with multiple training models, like decision trees, ensemble methods, support vector machines, and random forests.
    • Develop and train various neural networks using the TensorFlow library.
    • Delve into neural network architectures, like RNN, CNN, and deep reinforcement learning.

    This book is available only in the Paperback edition.

    About the Author

    Aurelien Geron was a co-founder and CTO of Wifirst, a leading wireless IPS in France. In addition, he also worked as a software engineer in Paris for a consulting firm.

    Publisher: O’Reilly
    Paperback Print Length: 566 pages

    You can buy this book from here .

    4. Neural Networks and Deep Learning: A Textbook

    Neural Networks and Deep Learning: A Textbook

    Author: Charu C. Aggarwal This book is an excellent guide for researchers, students, and practitioners of deep learning. It introduces you to classical and state-of-the-art models in deep learning. Also, the book primarily concentrates on algorithms and the theory of neural networks and deep learning. The author has added many real-world examples to help you learn how to design neural architectures for distinct types of problems. Furthermore, she has divided the book into three sections that are as follows:

    1. The basics of neural networks Chapters 1 and 2 talk about the basics of neural networks. Also, these chapters cover the relationship between neural networks and conventional machine learning. Moreover, they cover topics like matrix factorization, support vector machines, singular value decomposition, and linear regression.

    2. Fundamentals of neural networks Chapters 3 and 4 explain regularization and training in deep learning. While chapters 5 and 6 introduce you to restricted Boltzmann machines and radial-basis function (RBF) networks.

    3. Advanced topics in neural networks Chapters 7 and 8 discuss convolutional and recurrent neural networks. On the other hand, chapters 9 and 10 cover advanced neural network concepts, like neural Turing machines, deep reinforcement learning, generative adversarial networks, and Kohonen self-organizing maps. You can get this book in two different editions: Kindle and Paperback.

    About the Author

    Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Centre.

    Publisher: Springer

    Paperback Print Length: 497 pages

    You can buy this book from here .

    5. Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks

    Author: Robert J Marks II and Russell Reed

    This deep learning book focuses on Multilayer Perceptrons (MLP), a subset of feedforward artificial neural networks (ANN). ANNs are extensively used networks, having applications in various industries, like manufacturing and finance.

    This book is ideal if you wish to design and develop neural networks for various applications, ranging from forecasting to pattern recognition. The authors have provided an overview of the different aspects of the MLP methodology. You can read this book in Hardcover and Paperback editions.

    About the Author

    Russell Reed is a Research Assistant Professor of Electrical Engineering, and Robert J Mark II is a Professor of Electrical Engineering. Both are professors at the University of Washington.

    Publisher: MIT Press
    Paperback Print Length: 360 pages

    You can buy this book from here .

    6. Deep Learning (Adaptive Computation and Machine Learning Series)

    Deep Learning (Adaptive Computation and Machine Learning series)

    Author: Yoshua Bengio, Ian Goodfellow, and Aaron Courville

    This is one of the most comprehensive deep learning books for aspirants aiming to learn DL. It covers a broad spectrum of deep learning topics, research perspectives, and widely used DL techniques. This book covers a range of mathematical and other topics like algebra, information theory, machine learning, probability theory, and numerical computations.

    In addition, it introduces you to various industry-recognized deep learning techniques, like deep feedforward networks, optimization algorithms, sequence modeling, and convolutional networks. Finally, this book covers various theoretical topics, including the ones mentioned as follows:

    • Representation learning
    • Monte Carlo methods
    • Approximate interface
    • Linear factor models
    • The partition function
    • Autoencoders
    • Probabilistic models
    • Deep generative models

    You can read it in Hardcover and Kindle formats.

    About the Author

    Ian Goodfellow is a Research Scientist at Google, Yoshua Bengio is a Computer Science Professor, and Aaron Courville is a Computer Science Assistant Professor at the Universite de Montreal.
    Publisher: MIT Press
    Paperback Print Length: 800 pages

    You can buy this book from here .

    7. Deep Learning with Python

    Deep Learning with Python

    Author: Francois Chollet

    Entry-level or intermediate-level Python programmers can leverage this book to learn deep learning. This book helps you understand how to develop deep learning models using Python and the Keras library. With this book, you can easily understand various deep learning concepts as the author has explained them with exciting practical examples. In addition, the book will guide you to work with generative models, computer vision, and natural language processing.

    This book covers the following topics:

    • Deep learning from first principles.
    • Image-classification models.
    • Setting up a deep learning environment.
    • Text generation, neural style transfer, and image generation.
    • Deep learning for sequences and text.

    After reading this DL book, you will be able to develop the skills required to build your deep learning projects. Deep Learning with Python is available in two editions: Paperback and Kindle. With the book’s physical print, you also get its eBook in ePub, Kindle, and PDF formats for free.

    About the Author

    Francois Chollet works on deep learning at Google in California. In addition, he is a contributor to one of the well-known machine learning frameworks named TensorFlow and the creator of the Keras library.

    Publisher: Manning
    Paperback Print Length: 384 pages

    You can buy this book from here .

    8. Hands-on Deep Learning Algorithms with Python

    Hands-On Deep Learning Algorithms with Python: Master deep learning algorithms with extensive math by implementing them using TensorFlow

    Author: Sudharsan Ravichandiran

    The number 8 spot on our list of the best deep learning books is Hands-on Deep Learning Algorithms with Python. This book is a perfect guide for AI developers, machine learning engineers, data scientists, or anyone wishing to pursue a career in deep learning and neural networks. It is also helpful for those who are completely new to deep learning but have basic knowledge of Python and machine learning.

    The author has illustrated various deep learning algorithms in this book, ranging from basic to advanced levels. Also, the author explains the implementation of DL algorithms using TensorFlow, their applications, and the mathematics behind them.

    Initially, the book introduces you to an approach for building neural networks and TensorFlow fundamentals. Later, you will get acquainted with gradient descent and its variations, like Nadam, ASMGrad, and Adam.

    You can then learn to implement recurrent networks, like GRU, RNN, and LSTM. Also, you will understand how machines use CNN and capsule networks to interpret images. This deep learning book also explains different types of autoencoder, like DAE, VAE, CAE, and teaches you how to implement various Generative Adversarial Networks, like CycleGAN , CGAN, and StackGAN. This book is available in Kindle and Paperback formats.

    About the Author

    Sudharsan Ravichandiran is an artificial intelligence enthusiast, researcher, data scientist, and YouTuber. He holds a bachelor's degree in Information Technology from Anna University. Moreover, his interests include deep learning, reinforcement learning, computer vision, and natural language processing (NLP).

    Publisher: Packt Publishing Limited
    Paperback Print Length: 512 pages

    You can buy this book from here .

    9. Machine Intelligence: Demystifying Machine Learning, Neural Networks, and Deep Learning

    Machine Intelligence: Demystifying Machine Learning, Neural Networks and Deep Learning

    Author: Suresh Samudrala

    This is one of the best deep learning books for business and IT professionals aiming to hone their expertise in machine learning and artificial intelligence. It is also helpful for students seeking to develop a conceptual understanding of AI and ML. The author of this book has described various artificial intelligence and machine learning concepts concisely so that beginners can understand them with ease.

    He has used data tables and compelling examples to explain all concepts. Additionally, the author has covered deep learning, machine learning , and neural network algorithms and explained how mathematics is involved in machine learning algorithms.

    This book covers machine learning concepts thoroughly, and thus, it is the best choice for anyone looking for in-depth coverage of the subject. You can purchase this book in Paperback and Kindle editions.

    About the Author

    Suresh Samudrala has more than 20 years of experience in the IT industry. He holds a postgraduate degree in computer science from IIT Madras. In addition, his interest includes digital transformation through blockchain, machine learning, and quantum programming.

    Publisher: Notion press
    Paperback Print Length: 232 pages

    You can buy this book from here .

    10. Neural Networks for Pattern Recognition

    Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))

    Author: Christopher M. Bishop and Geoffrey Hinton

    It is among the modern deep learning books that introduce you to the use of neural networks in pattern recognition. Also, pattern recognition is one of the most significant applications of artificial neural networks. Neural Networks for Pattern Recognition is an excellent book for undergraduate students and practitioners aiming to develop artificial neural networks (ANNs) for tackling real-world problems.

    Besides introducing readers to types of neural nets, the authors have also described other topics like error functions, data pre-processing, density estimation, and Bayesian methods. This book covers:

    • The fundamentals of pattern recognition.
    • Approaches to model probability density functions.
    • Merits and properties of multi-layer perceptron (MLP).
    • Characteristics of radial basis function network models.
    • Discussion of generalization in neural networks.
    • Data processing.
    • Bayesian techniques and their applications to neural networks.

    You can read it in only the Paperback edition.

    About the Author

    Christopher M. Bishop is a Laboratory Director at Microsoft Research Cambridge. Also, he is a professor of computer science at the University of Edinburgh and a Fellow of Darwin College Cambridge. Geoffrey Hinton is a professor of computer science at the University of Toronto.

    Publisher: Clarendon press
    Paperback Print Length: 504 pages

    You can buy this book from here .


    Deep learning has added new capabilities to digital systems, like speech recognition, face detection, and so on. This technology is beneficial not only for the IT industry but also for other industries, including manufacturing, finance, entertainment, and health. With the increasing popularity of deep learning, many people today wish to develop skills in this technology.

    You can start learning DL skills with the best deep learning books mentioned in this article. You can read the description of each book carefully to pick the one or more that best suits your requirements.

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    Yes, you will have to learn Python for deep learning. As you will be involved in dealing with different sorts of data, models, validations, and visualization, you will have to use Python, as it comes with a variety of libraries that facilitate all these tasks.

    Yes, you can learn deep learning yourself. You just need is to have proper guidelines and resources to follow. Start with the books to get to grips with deep learning. Later, move on to developing simple and easy deep learning models.

    Yes, Deep learning with Python by Francois Chollet is an excellent book for beginners and intermediates. It helps learners understand deep learning models with Python and Keras library. It primarily focuses on the implementation of convolutional neural nets.

    Deep learning is the subset of machine learning. ML entails machines that learn and perform tasks themselves without being programmed. Meanwhile, deep learning deals with algorithms modeled on human brains.

    Some best Python libraries for deep learning are PyTorch, TensorFlow, Keras, Theano, Scikit Learn, and Pandas.

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