books about neural networks

• 15 Best Neural Network Books To Master Neural Network

Neural networks are a complicated term and a subject that a layman may not even understand. If we are to explain it in short, they are the neural networks in a computer that replicates the neural system of the brain to analyze data. The neural network is necessary for computing, storing, and analyzing data in all sectors of business.

Here is a quick look of top 15 best neural network books-

The students, who are learning neural networks, often find it difficult to understand and relate to. Hence, some reference  neural network books  are necessary.

Table of Contents

15 Best Neural Network Books

Here I have listed 15 books that will help you best understand the neural network. The books have theoretical as well as a practical aspect to it. The books will help you understand the practical application with examples as well.

The  neural network books  that I will list will also include in-depth pros and cons so that you can nitpick a book that best suits your needs. Read on, and you will get the best  neural network book  for you.

#1 Neural networks for pattern recognition [ check details & pricing ]

• Book Name: Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))
• Publisher: Oxford University Press
• Author: Christopher M. Bishop

The  neural network book  is a handbook and classic that depicts the theory and application of 25 years ago, i.e. when the concept was developed. The book is an excellent choice to build a base but it won’t be recommended to consider as a holy grail, rather, consider it as a reference book. The book is mostly available as a PDF. Here are a couple of features of the book-

• Solid statistical foundation for neural networks
• Focus is on the types of neural nets

#2 Neural Smithing [ check details & pricing ]

• Book Name: Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
• Publisher: Bradford Book
• Author: Russell Reed & Robert J MarksII

The book is the best buy if you want to find practical applications of the algorithms. The code and plots of the book are its highlights. The book uses maths, descriptions, snippets of pseudo code, and ANSI C to explain the concepts. It is of utmost use when you will do back propagation of error or an activation function. The one drawback of the book is that it is very detailed about the methods and over focuses on it. This is an ideal  neural network book   for beginners.

#3  Deep learning [ check details & pricing ]

• Book Name: Deep Learning (Adaptive Computation and Machine Learning series)
• Publisher: MIT Press
• Author: Ian, Yoshua, & Aaron

This book forms a bridge between the modern and classic concepts of deep learning. The milestones and highlights of neural networks have been discussed throughout the book. The different methods of neural networks are described in this  neural network book . The book doesn’t have any other concept apart from the methods, and hence, is not an overall glance of the subject.

#4 Neural Network and Deep Learning [ check details & pricing ]

• Book Name: Neural Networks and Deep Learning: A Textbook
• Publisher: Springer
• Author: Charu C. Aggarwal

This is a theory based  neural network book . The book includes coding and seven python scripts that discuss fundamental machine learning, neural network, or deep learning techniques on the MNIST dataset. The book has not only the real-life implications of the neural networks but also the theoretical explanations.

This makes it the perfect  neural network book for beginners . This book will be your holy grail if you wish to learn the basics of the machine and deep learning. The book, however, doesn’t contain many advanced and detailed snippets so if you wish to buy a book for the specialization of a topic, then this is not a recommended choice.

#5 Deep learning with python [ check details & pricing ]

• Book Name: Deep Learning with Python
• Publisher: Manning Publications
• Author: François Chollet

This book will take a practical approach to the theory. For every concept, Keras’ implementation of the technique is mentioned; although there are few sections for theory and practical application. The  neural network book pdf  will consist of a plethora of examples for deep learning with context to computer vision, text, and sequences.

The book also has a history of deep learning and the author’s insight into the same. The book is not detailed enough to explain all the subsections but will give a glance through the fundamentals of deep learning with adequate examples.

#6 Hands-on machine learning with Keras [ check details & pricing ]

• Book Name: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Publisher: O’Reilly
• Author: Aurélien Géron

This  neural network book pdf  is divided and organized into two parts. The first will cover the algorithms related to basic machine learning such as Support Vector Machines (SVMs), Decision, Trees, Random Forests, ensemble methods, and other fundamental unsupervised learning algorithms.

The examples for the Scikit-learn are included in the book as well. The second part will cover the theoretical aspects of deep learning via the TensorFlow library. The book is a major and epic read as a  neural network book.

#7 TensorFlow Deep Learning Cookbook [ check details & pricing ]

• Book Name: TensorFlow 1.x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python
• Publisher: Packt
• Author: Antonio Gulli & Amita Kapoor

This   book comes into the category of  neural network and fuzzy logic books  because it has zero to no theory and only coding. For TensorFlow users, this is the best  neural network book . It will teach you how to use TensorFlow concerning deep learning.

You will of course learn theory, algorithms, etc. about deep learning as well but its focus is solely on coding. The only criticism in this book is that there are some typos in coding, it’s an inconvenience but be aware of it while studying from it.

#8 Deep learning: a practitioner’s approach [ check details & pricing ]

• Book Name: Deep Learning: A Practitioner’s Approach
• Publisher: O’Reilly Media
• Author: Josh Patterson & Adam Gibson

This is one of the best  neural network books  as it takes a unique approach to teaching code as it uses java and DL4J libraries for the same as the conventional python. This is because java is used mostly at corporate levels. The book progresses in a way that it starts with the fundamental teachings of the machine and deep learning, and later, it will progress as java coding.

#9 Deep learning for computer vision [ Check details & pricing ]

• Book Name: Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras
• Author: Rajalingappaa Shanmugamani

This is one of the best  neural network books  and it will aid you in machine learning and resources to gather computer vision. The book is solely focused on computer visions of the current times and advanced methodologies.

#10 Deep Learning in Computer Vision [ check details & pricing ]

• Book Name: Deep Learning in Computer Vision: Principles and Applications (Digital Imaging and Computer Vision)
• Publisher: CRC Press
• Author: Mahmoud Hassaballah & Ali Ismail Awad

This is one of the best and  free neural network books  that have introduced a non-traditional way for solving image-related problems. These problems had been unaddressed before. Hence, this book has been a revelation. You can trust that it’s a good book and will help you immensely.

#11 Quantum Computing [ check details & pricing ]

• Book Name: Quantum Computing: Physics, Blockchains, and Deep Learning Smart Networks
• Author: Melanie Swan, Frank Witte, Renato P Dos Santos

Quantum physics is ever-changing and to keep up with the latest trend, you can try this  free neural network book . It goes beyond normal measures to cover the changes and new researches conducted in the field. The methods used for the understanding of the data and statistical data are also included in this  neural network book.

#12 Deep neuro-fuzzy systems [ check details & pricing ]

• Book Name: Deep Neuro-Fuzzy Systems with Python: With Case Studies and Applications from the Industry
• Publisher: Apress
• Author: Himanshu Singh & Yunis Ahmad Lone

If you wish to get an insight into the fuzzy and logical side of the  neural network books , then you can try this best  neural network book eBook . This contains the amalgamation and difference between the two systems of the models. Fuzzy logic and networking are easily explained in this book. These concepts are explained concerning the use of python to explain this.

#13 IA is a lifestyle [ check details & pricing ]

• Book Name: IA is a life style: Neural Network, Deep Learning, Machine Learning journal, notebook
• Publisher: Independently published
• Author: Motivz ML & Motivz arts

The book involves chapters that explain how the neural network and IA are the future. This maps the origin of the concept and explains them well theoretically. Some drawbacks of the books are that it cannot be used as a primary source of the education material. The book can be used as a reference and extra knowledge of the origin of the neural networks. This is one of the finest  neural network books eBooks.

#14 Computational vision and bio-inspired computing [ Check details & pricing ]

• Book Name: Computational Vision and Bio-Inspired Computing: ICCVBIC 2019 (Advances in Intelligent Systems and Computing (1108))
• Author: S. Smys, João, Valentina, & Abdullah

The book introduces the innovative and advanced research in biocomputing. The computational vision helps us to understand various fields and study. It is practical and theoretical. The  neural network book  will involve chapters about recent and future applications and the scope of biocomputing.

#15 Advanced Applied Deep Learning [ Check details & pricing ]

• Book Name: Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection
• Author: Umberto Michelucci

The deep learning concepts are taught concerning architectural designs. The intricate details and subtleties of the algorithms are explained in the chapters of this  neural network book  with ample examples and definitions. Advanced topics such as CNN are also taught in this book.

Conclusion- Which is the best Neural Network Book?

This was the 15 best neural network books which you can follow to learn deep learning and neural network. if you want to learn deep learning and neural network then these books can be a great point to start with.

Here we have reviewed many other books as well. Please have a look here-

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An Introduction to Neural Networks

James A. Anderson is Professor in the Department of Cognitive and Linguistic Sciences at Brown University.

An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas.

Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject.

The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.

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An Introduction to Neural Networks By: James A. Anderson https://doi.org/10.7551/mitpress/3905.001.0001 ISBN (electronic): 9780262315883 Publisher: The MIT Press Published: 1995

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• Introduction Doi: https://doi.org/10.7551/mitpress/3905.003.0001 Open the PDF Link PDF for Introduction in another window
• Acknowledgments Doi: https://doi.org/10.7551/mitpress/3905.003.0002 Open the PDF Link PDF for Acknowledgments in another window
• 1: Properties of Single Neurons Doi: https://doi.org/10.7551/mitpress/3905.003.0003 Open the PDF Link PDF for 1: Properties of Single Neurons in another window
• 2: Synaptic Integration and Neuron Models Doi: https://doi.org/10.7551/mitpress/3905.003.0004 Open the PDF Link PDF for 2: Synaptic Integration and Neuron Models in another window
• 3: Essential Vector Operations Doi: https://doi.org/10.7551/mitpress/3905.003.0005 Open the PDF Link PDF for 3: Essential Vector Operations in another window
• 4: Lateral Inhibition and Sensory Processing Doi: https://doi.org/10.7551/mitpress/3905.003.0006 Open the PDF Link PDF for 4: Lateral Inhibition and Sensory Processing in another window
• 5: Simple Matrix Operations Doi: https://doi.org/10.7551/mitpress/3905.003.0007 Open the PDF Link PDF for 5: Simple Matrix Operations in another window
• 6: The Linear Associator: Background and Foundations Doi: https://doi.org/10.7551/mitpress/3905.003.0008 Open the PDF Link PDF for 6: The Linear Associator: Background and Foundations in another window
• 7: The Linear Associator: Simulations Doi: https://doi.org/10.7551/mitpress/3905.003.0009 Open the PDF Link PDF for 7: The Linear Associator: Simulations in another window
• 8: Early Network Models Doi: https://doi.org/10.7551/mitpress/3905.003.0010 Open the PDF Link PDF for 8: Early Network Models in another window
• 9: Gradient Descent Algorithms Doi: https://doi.org/10.7551/mitpress/3905.003.0011 Open the PDF Link PDF for 9: Gradient Descent Algorithms in another window
• 10: Representation of Information Doi: https://doi.org/10.7551/mitpress/3905.003.0012 Open the PDF Link PDF for 10: Representation of Information in another window
• 11: Applications of Simple Associators Doi: https://doi.org/10.7551/mitpress/3905.003.0013 Open the PDF Link PDF for 11: Applications of Simple Associators in another window
• 12: Energy and Neural Networks Doi: https://doi.org/10.7551/mitpress/3905.003.0014 Open the PDF Link PDF for 12: Energy and Neural Networks in another window
• 13: Nearest Neighbor Classifiers Doi: https://doi.org/10.7551/mitpress/3905.003.0015 Open the PDF Link PDF for 13: Nearest Neighbor Classifiers in another window
• 14: Adaptive Maps Doi: https://doi.org/10.7551/mitpress/3905.003.0016 Open the PDF Link PDF for 14: Adaptive Maps in another window
• 15: The BSB Model Doi: https://doi.org/10.7551/mitpress/3905.003.0017 Open the PDF Link PDF for 15: The BSB Model in another window
• 16: Associative Computation Doi: https://doi.org/10.7551/mitpress/3905.003.0018 Open the PDF Link PDF for 16: Associative Computation in another window
• 17: Teaching Arithmetic to a Neural Network Doi: https://doi.org/10.7551/mitpress/3905.003.0019 Open the PDF Link PDF for 17: Teaching Arithmetic to a Neural Network in another window
• Afterword Doi: https://doi.org/10.7551/mitpress/3905.003.0020 Open the PDF Link PDF for Afterword in another window
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• © 2018

Neural Networks and Deep Learning

• Charu C. Aggarwal 0

IBM T. J. Watson Research Center, International Business Machines, Yorktown Heights, USA

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This book covers the theory and algorithms of deep learning and it provides detailed discussions of the relationships of neural networks with traditional machine learning algorithms.

The mathematical aspects are concretely presented without losing accessibility.

The book is written in a textbook style, and it includes exercises, a solution manual, and instructor slides. The depth and breadth of coverage are unique to the book.

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Table of contents (10 chapters)

Front matter, an introduction to neural networks.

Charu C. Aggarwal

Machine Learning with Shallow Neural Networks

Training deep neural networks, teaching deep learners to generalize.

• Radial Basis Function Networks
• Restricted Boltzmann Machines
• Recurrent Neural Networks
• Convolutional Neural Networks

Deep Reinforcement Learning

Advanced topics in deep learning, back matter.

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book  is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories:

The basics of neural networks:  Many traditional machine learning models can be understood as special cases of neural networks.  An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec.

Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.

Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10.

The book is written for graduate students, researchers, and practitioners.   Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

• Deep Learning
• Machine Learning
• Neural networks
• deep reinforcement learning
• autoencoder
• logistic regression
• pretraining
• backpropagation
• conjugate gradient-descent
• Kohonean self-organizaing map
• generative adversarial networks

Book Title : Neural Networks and Deep Learning

Book Subtitle : A Textbook

Authors : Charu C. Aggarwal

DOI : https://doi.org/10.1007/978-3-319-94463-0

Publisher : Springer Cham

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : Springer International Publishing AG, part of Springer Nature 2018

Softcover ISBN : 978-3-030-06856-1 Published: 31 January 2019

eBook ISBN : 978-3-319-94463-0 Published: 25 August 2018

Edition Number : 1

Number of Pages : XXIII, 497

Number of Illustrations : 128 b/w illustrations, 11 illustrations in colour

Topics : Artificial Intelligence , Information Systems and Communication Service , Processor Architectures

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Book description

Neural networks are at the very core of deep learning. They are versatile, powerful, and scalable, making them ideal to tackle large and highly complex Machine Learning tasks, such as classifying billions of images (e.g., Google Images), powering speech recognition services (e.g., Apple’s Siri), recommending the best videos to watch to hundreds of millions of users every day (e.g., YouTube), or learning to beat the world champion at the game of Go by examining millions of past games and then playing against itself (DeepMind’s AlphaGo). This lesson introduces artificial neural networks, starting with a quick tour of the very first ANN architectures, then covering topics such as training neural nets, recurrent neural networks, and reinforcement learning. This lesson will clarify what neural networks are and why you may want to use them.

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Table of contents

• Biological Neurons
• Logical Computations with Neurons
• The Perceptron
• Multi-Layer Perceptron and Backpropagation
• Training an MLP with TensorFlow’s High-Level API
• Construction Phase
• Execution Phase
• Using the Neural Network
• Number of Hidden Layers
• Number of Neurons per Hidden Layer
• Activation Functions
• Xavier and He Initialization
• Nonsaturating Activation Functions
• Batch Normalization
• Gradient Clipping
• Reusing a TensorFlow Model
• Reusing Models from Other Frameworks
• Freezing the Lower Layers
• Caching the Frozen Layers
• Tweaking, Dropping, or Replacing the Upper Layers
• Unsupervised Pretraining
• Pretraining on an Auxiliary Task
• Momentum Optimization
• Nesterov Accelerated Gradient
• Adam Optimization
• Learning Rate Scheduling
• Early Stopping
• ℓ1 and ℓ2 Regularization
• Max-Norm Regularization
• Data Augmentation
• Practical Guidelines
• The Architecture of the Visual Cortex
• Stacking Multiple Feature Maps
• TensorFlow Implementation
• Memory Requirements
• Pooling Layer
• Memory Cells
• Input and Output Sequences
• Static Unrolling Through Time
• Dynamic Unrolling Through Time
• Handling Variable Length Input Sequences
• Handling Variable-Length Output Sequences
• Training a Sequence Classifier
• Training to Predict Time Series
• Creative RNN
• Distributing a Deep RNN Across Multiple GPUs
• Applying Dropout
• The Difficulty of Training over Many Time Steps
• Peephole Connections
• Word Embeddings
• An Encoder–Decoder Network for Machine Translation
• Learning to Optimize Rewards
• Policy Search
• Introduction to OpenAI Gym
• Neural Network Policies
• Evaluating Actions: The Credit Assignment Problem
• Policy Gradients
• Markov Decision Processes
• Exploration Policies
• Approximate Q-Learning and Deep Q-Learning
• Learning to Play Ms. Pac-Man Using the DQN Algorithm
• Chapter 1: Introduction to Artificial Neural Networks
• Chapter 2: Training Deep Neural Nets
• Chapter 3: Convolutional Neural Networks
• Chapter 4: Recurrent Neural Networks
• Chapter 5: Reinforcement Learning

Product information

• Title: Neural networks and deep learning
• Author(s): Aurélien Géron
• Release date: March 2018
• Publisher(s): O'Reilly Media, Inc.
• ISBN: 9781492037347

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Neural Networks and Deep Learning

What this book is about

On the exercises and problems

• Perceptrons
• Sigmoid neurons
• The architecture of neural networks
• A simple network to classify handwritten digits
• Learning with gradient descent
• Implementing our network to classify digits
• Toward deep learning
• Warm up: a fast matrix-based approach to computing the output from a neural network
• The two assumptions we need about the cost function
• The Hadamard product, $s \odot t$
• The four fundamental equations behind backpropagation
• Proof of the four fundamental equations (optional)
• The backpropagation algorithm
• The code for backpropagation
• In what sense is backpropagation a fast algorithm?
• Backpropagation: the big picture
• The cross-entropy cost function
• Overfitting and regularization
• Weight initialization
• Handwriting recognition revisited: the code
• How to choose a neural network's hyper-parameters?
• Other techniques
• Two caveats
• Universality with one input and one output
• Many input variables
• Extension beyond sigmoid neurons
• Fixing up the step functions
• The vanishing gradient problem
• What's causing the vanishing gradient problem? Unstable gradients in deep neural nets
• Unstable gradients in more complex networks
• Other obstacles to deep learning
• Introducing convolutional networks
• Convolutional neural networks in practice
• The code for our convolutional networks
• Recent progress in image recognition
• Other approaches to deep neural nets
• On the future of neural networks

Appendix: Is there a simple algorithm for intelligence?

Acknowledgements

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10 Best Books on Neural Networks and Deep Learning, You Should Read

Are you looking for the Best Books on Neural Networks and Deep Learning ?. If yes, then read this article. In this article, I have listed the 10 Best Books on Neural Networks and Deep Learning . And I will also guide you to choose the best book for you.

Now without wasting your time, let’s get started-

Best Books on Neural Networks and Deep Learning

In this article, I have listed the most suitable Books on Neural Networks and Deep Learning for you.

1. Deep Learning (Adaptive Computation and Machine Learning series

Authors- Ian Goodfellow, Yoshua Bengio, Aaron Courville.

About Book –

This book is known as the “ Bible” of Deep Learning . The author Ian Goodfellow is the godfather of Deep Learning . That’s why this book is special for everyone who wants to learn the basics of Deep Learning.

This book is theoretical. This Deep Learning book is especially for those who want to learn the basics and theory part of Deep Learning.

This book begins with Machine Learning Basics , covers the mathematical and conceptual topics relevant to Deep Learning. This Deep Learning book covers linear algebra, probability theory and information theory, numerical computation .

After that, this book covers Modern Deep Learning Algorithms and Techniques. In that section, this book covers deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology.

This Book also describes applications of Deep Learning. Such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. In the end, this Deep Learning book describes the current research trends.

You Should Read this Book, if-

• You are an undergraduate or graduate student, professor, or one who wanna learn the basics of Deep Learning.
• You don’t have basic knowledge of Deep Learning.
• Or if you wanna learn the theory behind Deep Learning.

Where to Buy this Book?

You can buy on Amazon- Deep Learning (Adaptive Computation and Machine Learning series)

2. Deep Learning with Python

Authors- Francois Chollet

About Book-

This book is specially written for beginners and intermediate programmers . This book attracts me with its Keras implementation for each technique.

After reading this book, you will become Keras Expert and you can apply deep learning to your projects. This book is written in clear and easy language. You can understand concepts easily.

What’s inside the Book?

• Deep learning from first principles
• You will learn to set up your own deep-learning environment.
• This book covers Image-classification models
• You will learn Deep learning for text and sequences
• This book also covers Neural style transfer, text generation, and image generation .
• You have intermediate Python Skills with no previous experience with Keras, TensorFlow, or machine learning is required.
• You are interested in Keras Library or you want to learn Deep Learning by implementing.
• And if you want to learn quickly about how Deep Learning is used in computer vision, text, and sequence learning.

You can buy on Amazon- Deep Learning with Python

3. Neural Networks and Deep Learning

Authors- Charu C. Aggarwal   

This book covers both classical and modern models in deep learning . The primary focus is on the theory and algorithms of deep learning . To understand the full functionality of Deep Learning and neural networks, the theory is important.

This book covers all your questions related to Neural Networks. Like-

• Why do neural networks work?
• When do they work better than off-the-shelf machine-learning models?
• When is depth useful?
• Why is training neural networks so hard?
• What are the pitfalls?

This book also covers different applications of Deep Learning and Neural networks. This book is divided into 3 categories –

• The basics of neural networks.
• Fundamentals of neural networks.
• Advanced topics in neural networks.
• You are a graduate student, researcher, and practitioner .
• Or if you are a teacher , because in this book Numerous exercises are available along with a solution manual to aid in classroom teaching.

You can buy on Amazon- Neural Networks and Deep Learning .

4. Hands-On Deep Learning Algorithms with Python

Author- Sudharsan Ravichandiran 

In this book, you will understand basic to advanced deep learning algorithms , the mathematical principles behind them, and their practical applications . 

I personally love this book. And the reason is its simple and easy-to-understand language. This book explains the complex maths behind Deep Learning in a super-easy way.

This book will give you in-depth knowledge of the Basic to Advance Deep Learning algorithm with the mathematics behind each algorithm. Due to its simplicity, this book addicts you to learn the next chapter.

After reading this book-

• You will learn how to build a neural network from scratch.
• Along with that, you will learn the mathematics behind deep learning models .
• And you can implement popular Deep learning algorithms CNNs, RNNs, and others using Tensorflow.
• You are a Beginner and don’t have any prior knowledge in Deep Learning.
• You wanna learn coding concepts.
• Or you want to learn deep learning from scratch.

You can buy on Amazon- Hands-On Deep Learning Algorithms with Python .

5. Deep Learning: A Practitioner’s Approach

Author- Adam Gibson and Josh Patterson’s

Most of the books, I discussed uses Python code. But this book uses Java code and the DL4J library . Why this book uses Java? Because Java is mostly used in Programming Language especially in Big Companies.

This book covers the fundamentals of Machine Learning and Deep Learning . After covering fundamentals, this book covers J ava-based deep learning code examples using DL4J.

• If you have a specific project where you need to use Java Programming language.
• You want to understand how to operate the DL4J library.

You can buy on Amazon- Deep Learning: A Practitioner’s Approach 

6. Hands-On Machine Learning with Scikit–Learn and TensorFlow

Authors- Aurélien Géron

This book gives you a hands-on approach to learning by doing . It starts with the more traditional ML approaches (the Scikit-learn part) giving you a great deal of context and practical tools for solving all kinds of problems. This book has an excellent balance between theory/background and implementation.

This practical book shows you how even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.

This Book uses concrete examples, minimal theory, and two production-ready Python frameworks— Scikit-Learn and TensorFlow .

The first part of the book explains basic Machine Learning Algorithms. Support Vector Machine, Decision, Trees, Random Forests,  and many more . In that book, Scikit-learn examples for each of the algorithms are included.

In the second part, deep learning concepts through the TensorFlow library are explained.

In this book, you will learn-

• Explore the machine learning landscape, particularly neural nets
• Use Scikit-Learn to track an example machine-learning project end-to-end
• Explore s everal training models, including support vector machines, decision trees, random forests, and ensemble methods
• Use the TensorFlow library to build and train neural nets
• Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
• Learn techniques for training and scaling deep neural nets
• You have basic programming knowledge and beginner in Machine Learning and wants to start with the basics of coding.
• You are interested in the popular scikit-learn machine learning library.

You can buy on Amazon- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

7. TensorFlow 1.x Deep Learning Cookbook

Author – Antonio Gulli, Amita Kapoor

This book is written in a CookBook Style . That means a little theory and lots of code . This deep learning book is entirely hands-on and is a great reference for TensorFlow users.

In this book, you will learn how to efficiently use TensorFlow, Google’s open-source framework for deep learning.

You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes.

You will learn how to make Keras as a backend with TensorFlow . Along with that, you will learn with a problem-solution approach , how to implement different deep neural architectures to carry out complex tasks at work.

• You are interested in TensorFlow Library and likes CookBook Style reading.
• You have a basic knowledge of Deep Learning.

You can buy on Amazon- TensorFlow 1.x Deep Learning Cookbook .

8. Neural Networks for Pattern Recognition

Author- Christopher M. Bishop

This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition.

After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models .

This Book also covered various forms of error functions, principal algorithms for error function minimalization, learning, and generalization in neural networks.

It is designed as a text, with over 100 exercises , this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

• You want to dive deep into Pattern Recognition.

Where to Buy this Book-

You can buy on Amazon- Neural Networks for Pattern Recognition

9. The Hundred-Page Machine Learning Book

Author- Andriy Burkov

The “Hundred-Page Machine Learning Book” by Andriy Burkov, is, in my opinion, the best book for those working with machine learning libraries but who don’t have an understanding of the underlying science behind the libraries.

This book explains it in a very down-to-earth way. In this Book, some math is used, nothing too excessive, and should be easy for anyone with some mathematical experience to grasp.

It will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field.”

You can buy on Amazon- The Hundred-Page Machine Learning Book

10. Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

Author- Anirudh Koul, Siddha Ganju, Meher Kasam

Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin.

This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.

• Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite.
• Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral.
• Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies .
• Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning.
• Use transfer learning to train models in minutes.
• Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users.

You can buy on Amazon- Practical Deep Learning for Cloud, Mobile, and Edge .

So, these are the Top 10 Books on neural networks and deep learning . Now, I would like to give a brief introduction to Deep Learning.

What is Deep Learning?

Deep Learning is the subpart of Machine Learning . It is more robust than machine Learning. Deep Learning works on an Artificial Neural Network. Artificial Neural Network contains three layers- Input Layer, Hidden Layer, and Output Layer.

There may be n number of layers in the Hidden Layer. The deeper the Hidden Layer, the more accurate the result. That’s why it is known as Deep Learning.

Why Deep Learning is Popular?

Some features make Deep Learning more robust than Machine Learning-

• Deep Learning performs well on Large datasets, but Machine Learning can’t.
• In Deep Learning, you don’t need to feed all features manually like in Machine Learning. Feeding features manually is very time-consuming. This feature makes Deep Learning more powerful.
• Deep Learning can easily solve complex real-world problems, but Machine Learning can’t.

Due to these features, Deep Learning is getting more popular nowadays. Most people are using Deep Learning over Machine Learning.

Now let’s see which Book is good to learn Deep Learning-

Which Book on Neural Networks and Deep Learning Should You Choose?

For learning Deep Learning, you need to learn the theory part as well as the practical part . If you only focus on the practical and implementation part, you will miss some important theories. That’s why the book which balances both the theoretical and practical parts, is the best book for you.

That’s all.

In this article, you have discovered the Top 10 Books on neural networks and deep learning . Have you Bought or Read anyone of these Books?. If yes then tell your experience in the comment section.

I hope these Top 10 Books on neural networks and deep learning will help you to begin your Learning Journey.

All The Best.

Learn the Basics of Deep Learning Here

Deep Learning A to Z Basics

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Book: Neural Networks and Deep Learning (Nielsen)

• Last updated
• Save as PDF
• Page ID 3737

• Michael Nielson
• Y Combinator Research

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning and specifically will teach you about:

• Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
• Deep learning, a powerful set of techniques for learning in neural networks

Thumbnail: Stripe graphic (CC BY-SA 2.0; RCraig09 via Wikipedia).

9 Best Neural Network Books of All Time

Our goal : Find the best Neural Network books according to the internet (not just one random person's opinion).

• Type "best neural network books" into our search engine and study the top 5+ pages.
• Add only the books mentioned 2+ times.
• Rank the results neatly for you here! 😊 (It was a lot of work. But hey! That's why we're here, right?)

(Updated 2024)

As an Amazon Associate, we earn money from purchases made through links in this page.

Last Updated: Monday 1 Jan, 2024

• Best Neural Network Books

Deep Learning

Ian Goodfellow

Neural Networks for Pattern Recognition

Christopher M. Bishop

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Concepts, tools, and techniques to build intelligent systems.

Aurélien Géron

Deep Learning with Python

Francois Chollet

Neural Networks and Deep Learning

Charu C. Aggarwal

A Practitioner's Approach

Josh Patterson

Hands-On Deep Learning Algorithms with Python

Master deep learning algorithms with extensive math by implementing them using tensorflow.

Sudharsan Ravichandiran

TensorFlow 1.x Deep Learning Cookbook

Over 90 unique recipes to solve artificial-intelligence driven problems with python.

Antonio Gulli

Neural Smithing

Supervised learning in feedforward artificial neural networks.

Russell Reed

• Amazon Top 20 Books in Neural Networks - KDnuggets www.kdnuggets.com
• The 7 best deep learning books you should be reading right now - PyImageSearch pyimagesearch.com
• 10 Best Books on Neural Networks and Deep Learning in 2023 www.mltut.com
• 3 Must-Own Books for Deep Learning Practitioners - MachineLearningMastery.com machinelearningmastery.com
• 10 Best Deep Learning Books for Beginner & Experts in 2022 [Updated] hackr.io

How was this Neural Network books list created?

We searched for 'best Neural Network books', found the top 5 articles, took every book mentioned in 2+ articles, and averaged their rankings.

How many Neural Network books are in this list?

There are 9 books in this list.

Why did you create this Neural Network books list?

We wanted to gather the most accurate list of Neural Network books on the internet.

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What Are Neural Networks? A Beginner’s Complete Guide

March 25th, 2024 | 10 min. read

By Sylvain Rougemaille

In the last few years alone, advancements in artificial intelligence (AI) technologies have disrupted, for better or worse, how our world operates and our roles within in it.   

While these breakthroughs have sparked plenty of mainstream debate, especially following the introduction of Chat GPT in 2023, the inner workings of AI systems still elude most people. And with good reason – they’re complicated . One AI that is simultaneously everywhere but little understood is the artificial neural network (ANN), a subset of machine learning AI inspired by the processes in the brain. Given their prevalence, what are neural networks, anyway, and how do they work?   

Here at Pricefx, as a pricing software provider, we’re big advocates of AI (in fact, we’ve just added Gen AI to our roadmap in early 2024 ). To reap the full benefits of advanced AI technologies like neural networks, having a good grasp of their basic components – from their inner mechanics to applications in the real world – is fundamental, and we’re here to support that understanding.   

In this complete guide for beginners, we’ll break down what neural networks are, including how they work and where they’re used, and offer some key takeaways for using neural networks for business in the future.   

So, let’s dive in.   

What’s a Neural Network?   

Neural networks, or more appropriately, artificial neural networks, refer to a machine learning method in AI consisting of several layers of nodes, or artificial neurons. Using algorithms, neural networks learn from data over time to identify patterns, eventually drawing more accurate conclusions from new data as they improve.   

Its name, introduced in 1944 by Warren McCullough and Walter Pitts, comes from its likeness to human thinking processes, with its structure inspired by the network of neurons found in the advanced brain.  

Neural networks can solve problems that simpler algorithms can’t, but come naturally to humans, such as identifying faces and objects in images and videos, or making sense of and replicating natural (i.e., human) language.   

After an extensive training period, these systems can go on to make inferences from data without our explicit instructions. For example, after being exposed to enough examples, a neural network-enabled virtual assistant like Amazon Alexa at some point recognizes that a question phrased in various ways refers to the same thing, for example, categorizing “ how do I get to the airport?” and “ transportation options to the airport near me” as the same request.    

Right now, neural network capabilities generally fall into one of these categories: computer vision (detecting and interpreting visual data), speech recognition (converting human speech to text), and natural language processing (understanding human language), and recommendation systems (suggesting tailored options).  

What a Neural Network Isn’t: An Artificial Human Brain  

The term neural networks itself can be a bit misleading, contributing to a popular misconception that these systems can “think” on their own. While neural networks loosely model the human brain, they don’t mimic human thought. Artificial neural networks are learning, not thinking, machines, and still rely on training data from humans to complete their tasks.   

How Do Neural Networks Work?  

Basic architecture of neural networks    .

The neural network system’s basic structure can be broken down into three parts: an input layer, a hidden layer, and an output layer:  

Input Layer: The input layer is the point of entry for all training data into the neural network system and contains the input fields.   

Hidden Layer:  The hidden layer sits between input and output layers and isn’t directly visible. As the network’s computational center, the hidden layer is where data from the input layer is analyzed, categorized, and transformed for the output layer. The more hidden layers in a neural network, the “deeper” that network is.   

Output Layer: The output layer is the last layer of a neural network and produces the final predicted result, and, depending on the kind of task the network is working on, multiple results are possible.   

Underlying Mechanics of a Basic Neural Network  

While a bit overwhelming to take in at first glance, when broken down, a traditional neural network is in large part made up of dozens of simpler equations talking to each other, passing off data in a forward motion.   

To break this down further, a neural network system typically consists of several layers of nodes. Each node has its own activation function, and, in the simplest systems, that can take the form of a linear regression equation. Between nodes is a weighted connection, an indication of the degree of influence one node has on the other, that push data in the right sequence.   

For example, consider a neural network for a personalized recipe generator. The first layer of nodes could correspond to a user’s diet preferences. One node asks if a user eats meat, while others determine whether the user is vegan or vegetarian. If a user likes meat-heavy food, the weights connecting the nodes for Diet Preferences to the nodes representing meat-based recipes would be strong. On the hand, for vegetarian or vegan users, the weights between those same nodes would be weak or negative.  

So, in fact, a basic neural network isn’t structurally too complicated. However, more advanced models, namely deep learning neural networks, are made up of a remarkably varied set of functions and algorithms that extend far beyond linear regression equations.   

Simple Neural Networks vs. Deep Learning Systems   

You might have heard neural network and deep learning u sed interchangeably in the past, but they aren’t quite the same thing.   

As we know, a neural network is a machine learning method consisting of interconnected layers of nodes. A deep learning system, on the other hand, is a highly complex neural network with multiple hidden layers. In other words, deep learning systems are advanced versions of the classic neural network from the mid-20 th century, and most neural networks as we know them today are deep learning neural networks.   

Simple neural networks and deep neural networks differ in important ways, including:  

• Depth: Simple neural network systems have just one hidden layer, while deep learning systems have at least two up to thousands .  
• Types:  While a simple neural network is typically the feed-forward type, meaning the data only travels in one direction, deep neural networks offer more flexibility in how the data moves around and is processed by the system, taking the form of other types like recurrent (RNN) and recursive (RvNN)  neural networks.   
• Training Data Volumes: While deep learning systems require upwards of millions of data points for training purposes, simple neural networks need hundreds or thousands.   
• Cost: To accommodate massive amounts of training data, deep neural networks require more expensive hardware and significantly more memory and processing power than traditional neural networks do.  
• Implementation: Due to the complexity of the data sets in training and consequently a longer learning period, deep learning systems usually take longer to develop and set up than traditional neural networks.   

Deep learning neural networks can be found in many industries and for many purposes, like visual and speech recognition, natural language processing, recommendation engines, weather forecasts, and health care. Chat GPT itself relies on Large Language Models (LLM), which are deep RNN models, to produce convincing responses to user questions on a wide range of topics.   

How Neural Networks Are Used in the Real World: Common Uses  

From ChatGPT to Spotify, Amazon Alexa to Uber, neural networks quietly run in the background of our daily lives in more ways than we realize. Today, some of the most common applications of neural networks include:  

• Process and quality control , supporting higher production and safety standards in machinery-reliant industries like discrete and process manufacturing by identifying irregularities and suggesting future improvements.  
• Personalized recommendations , predicting what a user might like based on their historical buying decisions and web activity, such as personalized music playlists or suggested product groupings on e-commerce sites.  
• Price optimization , in which prices are dynamically adjusted based on an analysis of market trends, competitor pricing, and historical pricing data that helps ensure profitability and market competitiveness.  
• Targeted ads & content to support marketing campaigns , tapping into users’ behavioral data, buying history, and demographic information to suggest content or products that are likely to be engaged with.   
• Medical diagnosis , supporting medical facilities with interpreting and categorizing medical images and other complex clinical data to detect traces of illness or disease.   

Neural networks are employed by industry leaders across diverse industries, most visibly by tech giants. Few go into specifics, but Open AI continues to pioneer deep learning AI technologies that exhibit what they call “ human-level performance ”, Amazon stated it uses deep learning neural networks to forecast daily demand for its 400+ million products, and many others should follow suit.  

Considerations for Using Neural Networks in Business  

If you’re in a company thinking about using neural networks in its business operations, it’s vital to recognize what that decision would require of you and what to reasonably expect.    

Consider the current state of your company’s data. In aggregate, is your historical data representative of the (improved) outcomes you’re hoping to achieve with a neural network-enabled solution? The neural network trains on your company’s data, and you won’t want it replicating the same old logic that gave way to subpar results in the past. If it isn’t representative, ensure that the data is placed in the right context in your initial instructions to ensure the system is aligned with your goals. And even if it is, that data should be in good condition to enable the neural network to learn effectively and come up with accurate insights.     

Neural networks are also costly to build; the more parameters considered in the model, the more expensive they will be, particularly in the memory bandwidth needed to store them.  And in case you aren’t starting from scratch, keep in mind that gathering and cleaning millions of data points for neural network-enabled solutions is typically a time-intensive and costly project too.    

Lastly, think about how comfortable your organization is with accepting conclusions from a system with a decision-making logic that is, for the most part, inaccessible. Such is the nature of the black box, or opaque, systems, that define most neural networks out there today (although, several experiments on neural networks are underway to enable more interpretability of their results). Your answer to this question will in large part determine the long-term reliability of a neural networks-enabled solution for your company.   

Is Neural Network-Driven Pricing Right for You?  

In this article, we took you through the basics of neural network systems – what they are, how they work, and where they’re used – and left you with a few key considerations to keep in mind at your company before diving in.   

Have we left you curious about implementing neural networks in your pricing? Consider heading to our in-depth exploration of the implications of using neural networks for pricing AI optimization:  

Senior Product Manager , Pricefx

Sylvain Rougemaille PhD is Senior Product Manager at Pricefx based in France. He has 15 years of experience in the IT industry and AI. He obtained his PhD on Software Engineering applied to AI in 2008. Since then, he has participated the creation of two startups aiming at the diffusion of AI to solve complex industrial problems as aircraft optimization, genomic simulation, and ultimately price optimization. In 2015 he co-founded Brennus Analytics where he occupied the position of Chief Product Officer. The purpose of it was to bring the PO&M software market unrivalled optimization capabilities thanks to Multi-Agents’ AI. Since 2020 and its acquisition by Pricefx he is pushing pricing science even further as the Price Optimization and Science Manager.

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Quantum Physics

Title: optimizing quantum convolutional neural network architectures for arbitrary data dimension.

Abstract: Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. This approach is particularly attractive due to the absence of the barren plateau problem, a fundamental challenge in training quantum neural networks (QNNs), and its feasibility. However, a limitation arises when applying QCNNs to classical data. The network architecture is most natural when the number of input qubits is a power of two, as this number is reduced by a factor of two in each pooling layer. The number of input qubits determines the dimensions (i.e. the number of features) of the input data that can be processed, restricting the applicability of QCNN algorithms to real-world data. To address this issue, we propose a QCNN architecture capable of handling arbitrary input data dimensions while optimizing the allocation of quantum resources such as ancillary qubits and quantum gates. This optimization is not only important for minimizing computational resources, but also essential in noisy intermediate-scale quantum (NISQ) computing, as the size of the quantum circuits that can be executed reliably is limited. Through numerical simulations, we benchmarked the classification performance of various QCNN architectures when handling arbitrary input data dimensions on the MNIST and Breast Cancer datasets. The results validate that the proposed QCNN architecture achieves excellent classification performance while utilizing a minimal resource overhead, providing an optimal solution when reliable quantum computation is constrained by noise and imperfections.

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