Deep learning is a subset of artificial intelligence that is modeled after how humans learn. DL is a major component of data science, which includes statistics and predictive modeling. Deep learning is extremely beneficial to data scientists who must gather, analyze, and interpret large amounts of data; it expedites and simplifies the process.
If we see the statistics, we can say that artificial intelligence employment has a bright future. The US Bureau of Labor Statistics predicted a 13 percent increase in computer-related occupations between 2016 and 2026. According to the Economist Intelligence Unit (EIU), by 2025, 86 percent of financial services companies aim to boost their AI-related spending.
Summary
In this blog, you will come across some of the great books that are worth reading if you are planning to pursue your career in the field of deep learning and AI. So, let’s begin!
Best Deep Learning Books
1. Deep Learning in Python
Author – François Chollet
Publisher – Manning Publications
Latest Edition – Second
Formats Available – audiobook, eBook (DRM-free Kindle, ePub, and PDF), liveBook, and Paperback
Let’s start our list of the best deep learning books with Deep Learning in Python. This book introduces the area of deep learning using Python and the Keras framework. It is written by François Chollet, who is the founder of Keras and Google AI researcher, increases your comprehension through clear explanations and practical examples.
You’ll learn about difficult concepts and apply them to computer vision, natural language processing, and generative models. You’ll have the knowledge and ability to utilize deep learning in your own projects at the end of the course.
2. Deep Learning (Adaptive Computation and Machine Learning series)
Author – Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Publisher – The MIT Press
Latest Edition – First
Formats Available – Hardcover and Kindle
This book covers a variety of deep learning topics. It has relevant concepts of linear algebra, probability theory, information theory, numerical computation, machine learning, mathematical concepts, and conceptual backgrounds.
Deep feedforward networks, regularization, optimization algorithms, convolutional networks, and sequence modeling are just a few of the deep learning methodologies explained in the book. Natural language processing, speech recognition, computer vision, online recommendation systems, and other applications of deep learning are also covered.
3. Introduction to Machine Learning with Python
Author – Andreas C. Müller and Sarah Guido
Publisher – O’Reilly
Latest Edition – First
Formats Available – Kindle and Paperback
This is an easy-to-understand book on the introduction to machine learning and deep learning. It assumes no prior experience of coding or Python in particular, and it covers core concepts and applications of machine learning through examples, discussing various methodologies.
Introduction to Machine Learning with Python details using Python and sci-kit-learn to develop a robust machine learning application. The authors of the book, Andreas Müller and Sarah Guido, focus on the practical implications of employing machine learning algorithms rather than the mathematics underlying them. You’ll get even more out of this book if you’re familiar with the NumPy and matplotlib packages.
4. Hands-On Deep Learning Algorithms with Python
Author – Sudharsan Ravichandran
Publisher – Packt Publishing Limited
Latest Edition – First
Formats Available – Kindle and Paperback
This book walks you through a variety of popular deep learning methods, from the most fundamental to the most complicated, and shows you how to implement them using TensorFlow. Throughout the deep learning book, you’ll learn about each algorithm, the mathematical principles that underpin it, and how to put it into practice in the most efficient way possible.
The book begins by showing you how to create your own neural networks before introducing you to TensorFlow, a powerful Python-based machine learning and deep learning toolkit. After that, you’ll learn about gradient descent versions including NAG, AMSGrad, AdaDelta, Adam, and Nadam. It also contains a number of additional deep learning ideas.
5. Machine Intelligence: Demystifying Machine Learning, Neural Networks and Deep Learning
Author – Suresh Samudrala
Publisher – Notion Press
Latest Edition – First
Formats Available – Kindle and Paperback
This book is for IT and business professionals who want to learn more about these technologies but are put off by the complexity of the arithmetic involved. Students studying artificial intelligence and machine learning will benefit from this book because it provides a conceptual knowledge of algorithms as well as an industry perspective.
This book is a great place to start because it explains the fundamentals of machine learning algorithms in plain English with graphics, tables, and examples. It takes a straightforward approach that builds on the fundamentals, which will assist software engineers and students interested in learning more about the industry, as well as those who may have begun without the advantage of a structured introduction or solid foundation.
6. Neural Networks for Pattern Recognition
Author – Christopher M. Bishop and Geoffrey Hinton (Foreword)
Publisher – Clarendon Press
Latest Edition – First
Formats Available – Paperback
A complete treatment of feed-forward neural networks from the standpoint of statistical pattern recognition is presented in this book. After covering the fundamentals of pattern recognition, the deep learning book goes over modeling strategies for probability density functions, as well as the attributes and relative merits of the multi-layer perceptron and radial basis function network models.
The book on neural nets also explains why different types of error functions are used and goes over the main error function minimization strategies.
7. TensorFlow 1.x Deep Learning Cookbook
Author – Antonio Gulli and Amita Kapoor
Publisher – Packt Publishing Limited
Latest Edition – First
Formats Available – Kindle and Paperback
It is the first book to cover feedforward neural networks in depth from the standpoint of statistical pattern recognition. The book analyzes strategies for modeling probability density functions, as well as the qualities and benefits of multilayer perceptron and radial basis function network models, after introducing the fundamental ideas.
Error functions in various forms, ranking algorithms for error function minimization, learning and generalization in neural networks, and Bayesian approaches and their applications are also explained in this book.
Conclusion
Deep learning is becoming a part of the IT industry at a faster pace. Companies are working on Artificial Intelligence, Machine Learning, and Deep Learning to make the life of mankind easier.
This has increased the requirement of professionals who are proficient in deep learning. So, to ace your career in deep learning, you would need good resources and the books mentioned above would help you get the required knowledge.
Keep Learning!!
Aditya is a seasoned JavaScript developer with extensive experience in MEAN stack development. He also has solid knowledge of various programming tools and technologies, including .NET and Java. His hobbies include reading comics, playing games, and camping.