Jeff Heaton publishs several books on Artificial Intelligence. These books are written to be programming language independent. These books usually contain examples in the Python, R, Java, and C# programming languages. Refer to the specific book's description for a listing of what languages are included. In addition to these offline examples, online Javascript examples are also provided to allow the reader to experiment with these technologies using a web interface.

**All AI ebooks by Jeff Heaton**

This collection includes all AI ebooks written by Jeff Heaton. This includes volume 1, 2 and 3 of the Artificial Intelligence for Humans Series, the C#/Java versions of the Encog book, as well as the Introduction to the Math of Neural Networks book. This collection includes the individual ebooks. This collection is not offered in paperback form.

**Artificial Intelligence for Humans, Vol 1: Fundamental Algorithms**

A great building requires a strong foundation. This book teaches basic Artificial Intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series, but are very useful in their own right. The book explains all algorithms using actual numeric calculations that you can perform yourself. Artificial Intelligence for Humans is a book series meant to teach AI to those without an extensive mathematical background. The reader needs only a knowledge of basic college algebra or computer programming?anything more complicated than that is thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, and R.

**Artificial Intelligence for Humans, Vol 2: Nature-Inspired Algorithms**

Nature can be a great source of inspiration for artificial intelligence algorithms because its technology is considerably more advanced than our own. Among its wonders are strong AI, nanotechnology, and advanced robotics. Nature can therefore serve as a guide for real-life problem solving. In this book, you will encounter algorithms influenced by ants, bees, genomes, birds, and cells that provide practical methods for many types of AI situations. Although nature is the muse behind the methods, we are not duplicating its exact processes. The complex behaviors in nature merely provide inspiration in our quest to gain new insights about data.

**Artificial Intelligence for Humans, Vol 3: Deep Learning and Neural Networks**

Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.

**Introduction to the Math of Neural Networks**

This book introduces the reader to the basic math used for neural network calculation. This book assumes the reader has only knowledge of college algebra and computer programming. This book begins by showing how to calculate output of a neural network and moves on to more advanced training methods such as backpropagation, resilient propagation and Levenberg Marquardt optimization. The mathematics needed by these techniques is also introduced. Mathematical topics covered by this book include first, second, Hessian matrices, gradient descent and partial derivatives. All mathematical notation introduced is explained. Neural networks covered include the feedforward neural network and the self organizing map. This book provides an ideal supplement to our other neural books. This book is ideal for the reader, without a formal mathematical background, that seeks a more mathematical description of neural networks.

# Books About Encog

Encog is a open source machine learning framework that I created for Java and C#. I have a book on Encog for each language. These books can be downloaded free; however, I also sell paperback and Kindle versions.

**Programming Neural Networks with Encog3 in Java, 2nd Edition**

Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the Java programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering to gain new insights into data. Neural network architectures such as feedforward, self organizing maps, NEAT, and recurrent neural networks are introduced. This book also covers advanced neural network training techniques such as back propagation, quick propagation, resilient propagation, Levenberg Marquardt, genetic training and simulated annealing. Real world problems such as financial prediction, classifiction and image processing are introduced.

**Programming Neural Networks with Encog3 in C#, 2nd Edition**

Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the Java programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering to gain new insights into data. Neural network architectures such as feedforward, self organizing maps, NEAT, and recurrent neural networks are introduced. This book also covers advanced neural network training techniques such as back propagation, quick propagation, resilient propagation, Levenberg Marquardt, genetic training and simulated annealing. Real world problems such as financial prediction, classifiction and image processing are introduced.