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Deep Learning (Ian Goodfellow, et al)
Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

Neural Networks and Deep Learning (Michael Nielsen)
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 the core concepts behind neural networks and deep learning.

Deep Learning: Technical Introduction (Thomas Epelbaum)
This book presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent. For each network, their fundamental building blocks are detailed.

Artificial Neural Networks (Bernhard Mehlig)
This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. It describes the use of neural networks in machine learning: deep learning, recurrent networks, and other supervised and unsupervised machinelearning algorithms.

Discovering Musical Patterns by Artificial Neural Networks
Intended to introduce readers to the use of Artificial Neural Networks in the study of music, this book contains numerous case studies and research findings that address problems related to identifying scales, keys, classifying musical chords, etc.

Deep Learning with JavaScript: Neural Networks in TensorFlow.js
This book shows developers how they can bring Deep Learning technology to the web. Written by the main authors of the TensorFlow library, it provides fascinating use cases and indepth instruction for deep learning apps in JavaScript in your browser or on Node.

GANs in Action: Learning with Generative Adversarial Networks
This book teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. You'll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture.

Deep Learning and the Game of Go (Max Pumperla, et al)
This book teaches you how to apply the power of deep learning to complex reasoning tasks by building a Goplaying AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game.

Deep Learning with Python (Francois Chollet)
This book introduces the field of deep learning using the Python language and the powerful Keras library. It builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications.

Advanced Applications for Artificial Neural Networks (Adel E.)
In this book, highly qualified multidisciplinary scientists grasp their recent researches motivated by the importance of Artificial Neural Networks (ANN). It addresses advanced applications and innovative case studies for different fields using ANN.

Neural Network Design (Martin T. Hagan)
This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules. It emphasizes a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems.

Neural Networks with JavaScript Succinctly (James McCaffrey)
This book leads you through the fundamental concepts of neural networks, including its architecture, its inputoutput, tanh and softmax activation, backpropagation, error and accuracy, normalization and encoding, and model interpretation.

Deep Learning Tutorials (LISA Lab)
The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

Deep Learning in Neural Networks: An Overview (J. Schmidhuber)
It compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects.

Learning Deep Architectures for AI (Yoshua Bengio)
This book discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed.

Machine Learning, Neural and Statistical Classification (D. Michie)
Statistical, machine learning and neural network approaches to classification are all covered in this book to provide an objective assessment of the potential for machine learning algorithms in solving significant commercial and industrial problems, widening the foundation for exploitation of these and related algorithms.

Applied Artificial Neural Networks (Christian Dawson)
This book focuses on the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.

Neural Networks  A Systematic Introduction (Raul Rojas)
In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets.

An Introduction to Neural Networks (Kevin Gurney)
With an easy to understand format using graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multidimensional scientific data, and relates neural networks to statistics.

Neural Networks (Rolf Pfeifer, et al)
Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts.

Memristor and Memristive Neural Networks (Alex James)
This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: (1) Devices, (2) Models and (3) Applications.

Introduction to CNTK (Microsoft Cognitive Toolkit) Succinctly
This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks. It offers instruction on the basics of installing and running CNTK.

Keras Succinctly (James McCaffrey)
The goal of this book is to introduce you Keras, the one of the most popular and powerful libraries for building neural networks in Python. You'll learn how to build a convolutional neural network in Python!

A Brief Introduction to Neural Networks using Java
Introduces the Java programmer to the world of Neural Networks and Artificial Intelligence using SNIPE. Examples include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots.

Artificial Neural Networks  Models and Applications
This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this alwaysevolving machine learning technique. It contains chapters on basic concepts of artificial neural networks.

Artificial Neural Networks  Architectures and Applications
This book covers architectures, design, optimization, and analysis of artificial neural networks as well as applications of artificial neural networks in a wide range of areas including biomedical, industrial, physics, and financial applications.

Artificial Neural Networks  Methodological Advances and Apps
The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization. Advanced architectures for biomedical applications, which offer improved performance and desirable properties, follow.

Recurrent Neural Networks and Soft Computing (M. ElHefnawi)
Advanced information regarding the theory, concepts and applications of recurrent neural networks and the field of soft computing has been highlighted in this elaborative book. Additional topics in this vein are the application of AI techniques to electromagnetic interference problems, etc.

Recurrent Neural Networks for Temporal Data Processing
By presenting the latest research work the book demonstrates how realtime recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction.

Recurrent Neural Networks (Xiaolin Hu, et al)
This book investigates the following Recurrent Neural Networks (RNNs) models which solve some practical problems, together with their corresponding analysis on stability and convergence.

Neural Network Toolbox for MATLAB (Howard Demuth, et al)
It provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, timeseries forecasting, and dynamic system modeling and control.

Neural Networks Using C# Succinctly (James McCaffrey)
This book teaches you how to create your own neural network to solve classification problems, or problems where the outcomes can only be one of several values.

Introduction to Neural Networks for C#, 2nd Edition (Jeff Heaton)
This book introduces the C# programmer to the world of Neural Networks and Artificial Intelligence.

C++ Neural Networks and Fuzzy Logic (Valluru B. Rao, et al)
Provides a logical and easytofollow progression through C++ programming for two of the most popular technologies for artificial intelligence: neural and fuzzy programming.

Introduction to Neural Networks for Java, 2nd Edition (Jeff Heaton)
This book introduces the Java programmer to the world of Neural Networks and Artificial Intelligence.

Introduction to Neural Networks with Java 1st Edition (Jeff Heaton)
This book introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward backpropagation, Hopfield, and Kohonen networks are discussed.




















