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Machine Learning Using C# Succinctly

In Machine Learning Using C# Succinctly, you’ll learn several different approaches to applying machine learning to data analysis and prediction problems. Author James McCaffrey demonstrates different clustering and classification techniques, and explains the many decisions that must be made during development that determine how effective these techniques can be. McCaffrey provides thorough examples of applying k-means clustering to group strictly numerical data, calculating category utility to cluster both qualitative and quantitative information, and even using neural network classification to predict the output of previously unseen data.

Table of Contents

k-Means Clustering

Categorical Data Clustering

Logistic Regression Classification

Naïve Bayes Classification

Neural Network Classification

Neural Networks Using C# Succinctly

Neural networks are an exciting field of software development used to calculate outputs from input data. While the idea seems simple enough, the implications of such networks are staggering—think optical character recognition, speech recognition, and regression analysis. With Neural Networks Using C# Succinctly by James McCaffrey, you'll learn how to create your own neural network to solve classification problems, or problems where the outcomes can only be one of several values. Learn about encoding and normalizing data, activation functions and how to choose the right one, and ultimately how to train a neural network to find weights and bias values that provide accurate predictions. Download the book today!

Table of Contents

Neural Networks

Perceptrons

Feed-Forward

Back-Propagation

Training