Before listing out the best deep learning books, let’s elaborate on the concept of deep learning. Deep learning is a variant of Artificial Intelligence that allows computers to perform sorting tasks directly from the text, images, and sounds. It specializes in Machine Learning and is considered the popular domain of Artificial Intelligence that enables you to form multiple complexities.
Deep refers to the hidden layers in the network, and to achieve optimal results, it requires data in large numbers and considerable computing power. Because it is based on neural network topologies, they also call it the Deep Neural Network. The applications of the technology are tremendous, from automated driving, Image Recognition, Virtual Assistants and Media and Entertainment to Healthcare, Personalized services, and much more.
The concept of Deep Learning is relatively easy if you know a thing or two about Machine Learning. If you have good knowledge of Algebra, Linear, Programming Language, Statistics, and Probability, it won’t be hard for you to understand the fundamentals of deep learning.
Best Deep Learning Books
Team Duniakagyan has hand-picked some of the best deep learning books. So, before purchasing, go through our list, which might be helpful for informed decisions.
1. Deep Learning For Natural Language Processing: Applications of Neural Networks To Machine Learning Tasks by Pearson Learn IT
Sign up for this online video course to beef up your knowledge of deep learning of natural language processing right from your device anytime, anywhere. The online video lessons are designed to provide you with a lot of information quickly. The content is well-structured and easy for you to comprehend.
It teaches you the fundamentals of deep learning for natural language processing through case studies. The course explains concepts in a step-by-step manner for beginners to grasp concepts quickly. There are exercises as well to help you test your understanding of the concept. The videos in the course are delivered by experts with years of experience in industry and academics.
buy this book from here
2. Hands-On Deep Learning Algorithms with Python by Sudharsan Ravichandran
From fundamentals to advanced principles of Algorithms with their practical applications, this book walks you through it all. The book begins from the basics and gradually moves to an advanced level. It explains the major concept of algorithms by using the TensorFlow Chart, for stickier learning.
You can vouch for the book for grasping the fundamentals of algorithms and the mathematical ideas that underpin them. Also, you’ll learn how to implement them. The book introduces you to TensorFlow, which is a powerful python-based library to access deep learning and machine learning concepts. It comes in handy in learning how to build neural networks.
The book offers great insights into RNNs, LSTM, Speed descent variants such as Adadelta, Adam, and Nadam, generating lyrics with RNN, convolutional, and capsule networks for image recognition tasks and semantic words and documents as CBOW, AND PV-DM. You will also learn about various GANS and autoencoders, such as contractive encoders and VAE.
By the end of this book, you will be able to implement deep learning on various projects.
- Author: Sudarshan Ravichandran
Buy this book from here
3. Deep Learning with Python by Francois Chollet
As one of the popular deep learning books, it’s tailormade for beginners and intermediate learners keen on understanding the concept of convolutional neural networks. The book provides you with an in-depth understanding of Keras and studies the basic difference between AI and deep learning.
A list of practical code examples provided in the book will help you learn new concepts faster. You can even apply them for hands-on learning. This book will make you understand the concept of deep learning through Keras, and by the end of this book, you can implement deep learning on various projects.
- Author: Francois Chollet
Buy this book from here
4. Deep Learning [Adaptive Computation and Machine Learning Series] By Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Francis Bach
This book will assist you in comprehending the fundamentals of deep learning. It’s a lengthy book covering topics, such as linear algebra, probability theory, numerical computation, information theory, and machine learning. The book explains deep learning techniques, including deep forward networks, optimization, algorithms, regularization, and convolutional networks.
you can also count on the book for learning speech recognition, natural language processing, bioinformatics, video games, and more. It covers the basic theories, such as linear factor models, representation learning, autoencoders, Monte Carlo methods, and deep generative models. The book is a great resource for software engineers who want to develop products using deep learning.
- Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Francis Bach
Buy this from here
5. Practical Convolutional Neural Networks by Mohit Sewak, Md.Rezaul Karim and Pradeep Pujari
This book is beneficial for those who want to learn CNN architecture. It elaborates on the deep neural networks with image classification, helping you understand the first CNN human face detector. Furthermore, the authors teach you essential concepts, such as transfer learning with CNN and Autoencoders to build attractive models.
You can rely on the book for learning advanced methods, such as algorithms for object detection, image captioning, segmentation, attention mechanisms for vision, and much more. Towards the end of the book, you will learn ways to implement CNN models for your professional projects.
They extensively use CNN (Convolutional Neural Networks) for applications like visual recognition systems, medical discoveries, innovative eCommerce, self-driving cars, etc. By the end of the book, you will specialise in the applications of CNN techniques, and performing extreme datasets while using different CNN techniques to build ConvNet Models.
- Authors: Mohit Sewak, Md.Rezaul Karim and Pradeep Pujari
Buy this from here
6. Neural Networks for Pattern Recognition By Christopher M.Bishop
As one of the sought-after deep learning books, it teaches you how to think about neural networks in terms of statistical pattern recognition. It covers the basics of modelling probability density functions and the qualities and advantages of multilayer perceptron and radical function network models.
The book will help you understand error functions in various forms, ranking algorithms for error function minimization, learning and generalization in neural networks, Bayesian approaches and applications, and so on. Plus, you will find multiple exercises that will help you test your practical knowledge and are essential for those engaged in neural computation and pattern recognition.
- Author: Christopher M Bishop
Check out this book from here.
7. Neural Networks and Deep Learning: A textbook by Charu C. Aggarwal
This book focuses on the classical and modern models in deep learning. The author discusses different applications and how neural applications are designed to solve various problems.
The book is organized into three sections: Neural Networks Fundamentals, Neural Networks Advanced Topics, and Neural Networks Fundamentals. It covers various topics, such as machine translation, recommender systems, image classifications, image captioning, etc.
Graduates, researchers, and practitioners will benefit from this book, which provides a better grasp of neural networks and deep learning.
Buy this book from here
8. Machine Intelligence: Demystifying Machine Learning, Neural Networks and Deep Learning by Suresh Samudrala
With the help of pictures, data tables, and charts, this book introduces the fundamentals of machine learning algorithms. The author covers the concepts of classical machine learning, neural networks and begins from the basics. The idea is to help software engineers understand it properly before implementing it on their projects.
This book is also useful for IT students and professionals who want to learn these concepts without dealing with complicated mathematical problems.
This book is available for purchase here.
9. Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks by Russell Reed, Robert J MarksII
The book focuses on artificial neural networks, which are known as multilayer perceptrons [MLP]. These neural networks are widely used in finance for forecasting, manufacturing for process control, and in the field of science for speech and recognition.
The book covers all the aspects of MLP methodology, beginning with what MLPs are and detailing technical factors affecting performance. It can be widely used as a tool kit by readers interested in engaging with several kinds of problems.
Check out this book from here.
10. Deep Learning: A Practitioner’s Approach by Josh Patterson
This book provides practical information and helps you build an efficient understanding of the field of deep learning. The author explains concepts on deep learning and provides methods and strategies to understand the deep learning architecture. You can learn about convolutional and Recurrent deep network fundamentals. Understand how to make use of Vectorization techniques and deep learning DL4J on spark and Hadoop.
Buy this book from here.
That’s the end of our list of deep learning books. Deep Learning has gained potential in real-world applications. It is the best platform for producing actionable results and provides enormous benefits personally and professionally to those involved in this field. So which book are you starting to read today?
People are also reading: