• Title Practical Artificial Intelligence Programming in Java, Fifth Edition
• Author(s) Mark Watson
• Publisher: Leanpub (Fifth Edition, July 2020); eBook (Create Commons Edition)
• License(s): CC BY-NC-ND 3.0 US
• Hardcover/Paperback: N/A
• eBook: PDF, ePd, Kindle, etc.
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
• Share This:  

Book Description

This book uses both best of breed open source software and the author's own libraries to introduce the reader to Artificial Intelligence (AI) technologies like genetic algorithms, neural networks, expert systems, machine learning, and statistical natural language processing (NLP).

• N/A

• Amazon

• Artificial Intelligence, Machine Learning, and Logic Programming
• Advanced Java Programming
• Neural Networks
• Machine Learning

• Practical Artificial Intelligence Programming in Java, Fifth Edition (Mark Watson)
• The Mirror Site (1) - PDF, ePub, Kindle, etc.
• The Mirror Site (2) - PDF (222 pages, 1.2 MB)

• 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.

• Introduction to Neural Networks with Java (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.

• Programming Neural Networks with Encog3 in Java (Jeff Heaton)

  This book focuses on using the neural network capabilities of Encog with the Java programming language. It begins with an introduction to the kinds of tasks neural networks are suited towards.

• 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.

• 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 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 in-depth instruction for deep learning apps in JavaScript in your browser or on Node.

• 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.

• Machine Learning with Neural Networks (Bernhard Mehlig)

  This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. It provides comprehensive coverage of neural networks, their evolution, their structure, their applications, etc.

• 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.

• 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.

• Deep Neural Networks and Data for Automated Driving

  This open access book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving and artificial intelligence.

• Deep Learning for Coders with Fastai and PyTorch

  This book show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

• 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. It is aimed at readers who seek an overview of the field or who wish to deepen their knowledge.

• 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. It is a systematic introduction to neural networks, biological foundation.