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Neural Networks and Deep Learning
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  • 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 Go-playing 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 (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.

  • 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 machine-learning algorithms.

  • 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 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.

  • Neural Networks with JavaScript Succinctly (James McCaffrey)

    This book leads you through the fundamental concepts of neural networks, including its architecture, its input-output, tanh and softmax activation, back-propagation, 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.

  • R Deep Learning Essentials (Joshua F. Wiley)

    This book offers you the essentials every developer needs to get started - from the packages you need to have for your side, building models related to neural networks, prediction, and deep prediction, to fine tuning and optimizing everything you have.

  • 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!

  • 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.

  • 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 multi-dimensional 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.

  • A Brief Introduction to Neural Networks using Java

    This book introduces the Java programmer to the world of Neural Networks and Artificial Intelligence using SNIPE.

  • 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.

  • 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 always-evolving 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 real-time 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, time-series 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 easy-to-follow 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.

  • Neural Network Programming with Java (Alan M.F. Souza, et al)

    This book gives you a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java. No previous knowledge of neural networks is required as this book covers the concepts from scratch.

  • Python Deep Learning (Valentino Zocca, et al)

    Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. This book will give you all the practical information available on the subject, including the best practices, using real-world use cases.

  • Neural Networks

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