Deep Learning (DL) and Artificial Intelligence (AI) have made the future of self-driving automobiles and virtual assistants a reality. The innovations of DL can be found everywhere, on our smartphones, streaming services like Netflix, in virtual reality games, and more. The power of deep learning to make computers think, act, and behave like humans is remarkable. Given the rapid growth of computers and technology, newcomers and old professionals seek to learn this new domain of deep learning. As a result, many people opt for this field and try to contribute to the future. A career in deep learning will benefit young innovative minds to grow personally and professionally. Now, let’s learn what deep learning is and some of the best books for deep learning.
What is Deep Learning?
Deep learning is a subset of machine learning and artificial intelligence. This domain allows computers to process classification tasks directly from data like texts, images, and sounds. It is based on artificial neural networks in which multiple layers are processed, thus, called deep learning to extract higher-level features from data. Deep learning is the process of leveraging data analytics and the latest gains in computing power to work even faster than human minds. Studying deep learning can be hectic if you are not on the right track and don’t have the right resources. Many books have focused on deep learning in the last few years, but which one to pick? Here is a list of top deep learning books that may help you start with deep learning.
List of top Deep Learning books
- The Hundred-Page Machine Learning Book by Andriy Burkov
To get into deep learning, you need to know about machine learning. And the best way to learn machine learning is by reading & understanding the algorithms and implementing them. Now, several books for deep learning & machine learning are out in the market, as the field of AI is vast, and so is the variety of books. Also, many things overlap in ML & DL. Thus, you want to grasp a good understanding from the beginning. The book ‘The Hundred-Page Machine Learning Book’, written by Andriy Burkov, an ML expert, is a practical guide to getting started with ML. The first few chapters focus on ML formulation, notations, and key terminologies. Thus, beginners and newcomers in the field can opt for this book. Then the coming chapters analyze the most important algorithms in ML and more advanced topics. Though this book contains only one chapter about neural networks, it indeed serves as a building block for DL.
- Deep Learning with Python by François Chollet
Written by François Chollet, the creator of Keras and a Google AI researcher, ‘Deep Learning with Python’ explains the concepts of DL using the Python language and Keras library. It is one of the best deep learning books that provide a good understanding of the concepts through intuitive explanations and practical examples. This book encourages beginners and intermediate programmers to understand DL in-depth through extensive descriptions of implementing convolutional neural networks (CNNs). In overview, this book is divided into two parts, first, the fundamentals of DL, and two, DL in practice. The fundamentals cover high-level crucial concepts in DL, and practice mostly covers applications such as DL for computer vision, text & sequences, advanced DL practice, and generative DL. By finishing this book, you’ll have the hands-on skills to apply deep learning models in your projects. You can buy this neural network book online.
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- Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
‘Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow’ will guide you through acquiring basic concepts of DL so that anyone can use simple and efficient tools to implement programs capable of learning from data. Written by Aurélien Géron, a machine learning consultant, this deep learning book comprises concrete examples with minimal theory and two production-ready Python frameworks, Sklearn and TensorFlow, to master the use of DL. This book provides an intuitive understanding of the concepts & tools for building intelligent systems using Scikit & Tensorflow.
You need prior programming knowledge to apply what you learn from this book. The exercises range from simple linear regression to processing deep neural networks, including CNN and transfer learning. This book on deep learning helps you to explore ML, particularly neural networks, and other training models like support vector machines (SVM), decision trees, and ensemble methods. Also, you learn the neural network architectures of CNN, recurrent neural network (RNN), and deep reinforcement learning. Then, you can use Sklearn to track end-to-end ML projects and TensorFlow to build & train the neural networks. The book retails at ₹2,600 for the second updated edition.
- Deep Learning from Scratch: Building with Python from First Principles by Seth Weidman
‘Deep Learning from Scratch: building with Python from First Principles’ is a handbook to build your foundation of deep learning. The author Seth Weidman is a data scientist who has a unique way of explaining the concepts with a visual representation of the working of the algorithm, a mathematical explanation of why the algorithm works, and a pseudocode implementation of the algorithm. It is one of the best books on deep learning that teaches how to apply multiplayer neural networks and convolutional networking. Also, it provides a comprehensive introduction to DL for data scientists & software engineers. It focuses on how neural networks work using the first principles hence, the name. The book starts with DL basis and then moves to extensive details of important advanced networks of CNN & RNN. It has a dedicated chapter on extensions and PyTorch, explaining loss function, momentum & weight initialization, etc, and how to implement DL models with PyTorch & unsupervised learning, respectively.
- Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach
‘Deep Learning (Adaptive Computation and Machine Learning series)’ is on the list of top books for deep learning that presents an in-depth understanding of deep learning, written together by four computer scientists and deep learning enthusiasts. Ian J. Goodfellow, a research scientist in DeepMind, who invented generative adversarial networks (GANs). Yoshua Bengio is one of the leading experts in AI, a professor at the Université de Montréal & head of the Montreal institute for learning algorithms. Aaron Courville is an Associate professor at the Université de Montréal & member of the Mila-Quebec Artificial Intelligence Institute. Francis Bach is a world-renowned ML expert and researcher at the National institute for research in digital science and technology (INRIA). The books combine a wide range of concepts and topics in deep learning. It is divided into three parts, first, applied math & ML basics; second, modern practices in DL, and third, DL research. The first part has a firm mathematical foundation and covers linear algebra, probability theory, information theory, and numerical computation. In the second part, the book explains deep feedforward networks, regularization, optimization, CNN, sequence modeling, and applications. In the third and final part, the book offers insight into linear factor models, autoencoders, representation learning, Monte Carlo methods, structured probabilistic models, confronting partition function, and deep generative models. This book is an excellent addition to deep learning books, which is available online.
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- Grokking Deep Learning by Andrew W. Trask
‘Grokking Deep Learning’ talks about the science behind DL by explaining the building and training of neural networks. The author Andrew W. Trask, a PhD student at Oxford University and a research scientist at DeepMind, focused on unveiling the science under the hood so that you understand every detail of training a neural network. This book emphasizes using Python and NumPy to train neural networks to see & understand images, translate text into different languages, etc, to master the working of DL frameworks. Beginners can see this neural network and deep learning textbook as a mentor, as it walks through every aspect of the why, what, and how of deep learning models. In the end, you get a chapter, ‘Where to go from here’ in which the factors of DL are explained and how DL will be a promising career for you.
- Deep Learning with PyTorch by Eli Stevens, Luca Antiga, Thomas Viehmann
‘Deep Learning with PyTorch’ is among the most popular machine learning and deep learning books. This practical book dynamically gets you to build real-world projects from scratch. The authors are Eli Stevens, a software engineer & CTO of a startup company building software for radiology, Luca Antiga, the co-founder & CEO of an AI engineering company and a constant contributor to PyTorch, and Thomas Viehmann, a core PyTorch core developer and an ML & PyTorch specialist trainer & consultant. The book teaches you how to create neural networks & DL systems with PyTorch. It covers some of the best practices for DL pipeline and basics and takes you to larger projects. The highlight of this book is an elaborated neural network designed for cancer detection. This is a whole package for deep learning books where you discover ways to train networks with limited inputs and then focus on the diagnosis to fix problems in the network. Eventually, you will learn ways to improve the network & architecture, perform fine-tuning, and the results with augmented data.
