Artificial Intelligence for Humans
My Artificial Intelligence for Humans (AIFH) series focused on building algorithms from scratch in languages such as Java, C#, Python, and R. Volumes 1 and 3 have evolved into my Applications of Deep Neural Networks book.
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.
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.
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.
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.
Older Books
These are older books covering such topics as building pre-deep learning neural networks from scratch, Second Life, webscraping (before APIs became common), building computers, and even introductory Java programming. I also documented my open-source Encog machine learning library for Java and C#.- 1998, December - Sams Teach Yourself Visual C++ 6 in 21 Days
- 2002, February - Programming Spiders, Bots, and Aggregators
- 2002, September - Jstl-Jsp Standard Tag Library Kick Start
- 2003, June - BEA WebLogic Server 8 For Dummies
- 2005, November - Introduction to Neural Networks with Java [Download]
- 2006, May - Java for the Beginning Programmer [Download]
- 2006, June - Build a Computer from Scratch [Download]
- 2007, April - HTTP Programming Recipes for C# [Download]
- 2007, April - HTTP Programming Recipes for Java Bots [Download]
- 2007, July - Scripting Recipes for Second Life [Download]
- 2007, September - Introduction to Textures, Animation Audio and Sculpting in Second Life [Download]
- 2007, December - Introduction to Linden Scripting Language for Second Life [Download]
- 2008, October - Introduction to Neural Networks with Java, 2nd Edition [Download]
- 2008, October - Introduction to Neural Networks for C#, 2nd Edition [Download]
- 2010, March - Programming Neural Networks with Encog 2 in Java [Download]
- 2010, March - Programming Neural Networks with Encog 2 in C# [Download]
- 2011 - Programming Neural Networks with Encog3 in Java, 2nd Edition
- 2011 - Programming Neural Networks with Encog3 in C#, 2nd Edition
- 2022 - Applications of Deep Neural Networks with Keras