Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new techlogies 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 techlogies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.
Jeff Heaton, PhD, is a data scientist and indy publisher. Specializing in Python, R, Java and C#, he is an active technology blogger, open source contributor, and author of more than ten books. His areas of expertise include predictive modeling, data mining, big data, business intelligence, and artificial intelligence. Jeff holds a Master's Degree in Information Management from Washington University and a PhD in computer science from Nova Southeastern University in computer science. He is the lead developer for the Encog Machine Learning Framework open source project, a senior member of IEEE, and a fellow of the Life Management Institute (FLMI).