: A probabilistic technique for approximating the global optimum of a given function. Practical C# Applications
: The most common type of network where information moves in only one direction from input to output.
: Techniques for optimizing network size.
: A type of unsupervised learning used to produce a low-dimensional representation of input space. Training Techniques
The book by Jeff Heaton serves as a bridge for C# developers looking to enter the world of Artificial Intelligence without getting bogged down in academic jargon. This edition, published on October 2, 2008, by Heaton Research, Inc., provides 428 pages of practical theory and source code designed to help programmers implement complex machine learning models. Core Concepts and Architectures
: Creating internet bots capable of scanning and interpreting web page data. Chapter Breakdown
: A probabilistic technique for approximating the global optimum of a given function. Practical C# Applications
: The most common type of network where information moves in only one direction from input to output.
: Techniques for optimizing network size.
: A type of unsupervised learning used to produce a low-dimensional representation of input space. Training Techniques
The book by Jeff Heaton serves as a bridge for C# developers looking to enter the world of Artificial Intelligence without getting bogged down in academic jargon. This edition, published on October 2, 2008, by Heaton Research, Inc., provides 428 pages of practical theory and source code designed to help programmers implement complex machine learning models. Core Concepts and Architectures
: Creating internet bots capable of scanning and interpreting web page data. Chapter Breakdown
