Description: Neural networks have fallen only to rise again twice. The latest incarnation borrows much from the past but adds elements to support deep learning. This article demonstrates the state of the art in deep neural network programming.

Description: A discussion on supervised learning--the class of machine learning where a model is trained to produce a specific result for a given input.

Description: The author presents autoencoders in a clear manner and then goes on to show an intriguing application where an autoencoder can alert us to events out of the ordinary.

Description: An exploration of moving predictive modeling capabilities in R to real time operations.

Description: A description of how to use Deep Learning and TensorFlow

Description: Bayesian networks are one of many models that are useful for predictive modeling. This article introduces the concept of Bayesian network and shows how time-series data can be modeled for a Bayesian network.

Description: A look into how machine learning models implement classification and regression at a deeper level. Covers topics such as multi-regression, softmax, and ROC charts.

Description: This article shows how to use a RBF Neural network to predict if a breast tumor is malignant. The network is trained using a genetic algorithm that uses C# multi-threading.

Description: This article introduces SymPy, a computer algebra system (CAS) for the Python programming language.

Description: Deep learning is a method that allows neural networks to be constructed with more than just one or two hidden layers. These layers can then be trained independently and simultaneously of each other. This article provides an introduction to deep belief neural networks.