Title Page Copyright and Credits Hands-On Machine Learning with C# Packt Upsell Why subscribe? PacktPub.com Contributors About the author About the reviewer Packt is searching for authors like you Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Reviews Machine Learning Basics Introduction to machine learning Data mining Artificial Intelligence Bio-AI Deep learning Probability and statistics Approaching your machine learning project Data collection Data preparation Model selection and training Model evaluation Model tuning Iris dataset Types of Machine Learning Supervised learning Bias-variance trade-off Amount of training data Input space dimensionality Incorrect output values Data heterogeneity Unsupervised learning Reinforcement learning Build, buy, or open source Additional reading Summary References ReflectInsight – Real-Time Monitoring Router Log Viewer Live Viewer Message navigation Message properties Watches Bookmarks Call Stack Searching through your messages Advanced Search Time zone formatting Auto Save/Purge Example ReflectInsight Utilities:  Watches Software Development Kit Configuration editor Overview XML configuration Dynamic configuration Main Screen Summary Bayes Intuition – Solving the Hit and Run Mystery and Performing Data Analysis Overviewing Bayes' theorem Overviewing Naive Bayes and plotting data Plotting data Summary References Risk versus Reward – Reinforcement Learning Overviewing reinforcement learning Types of learning Q-learning SARSA Running our application Tower of Hanoi Summary References Fuzzy Logic – Navigating the Obstacle Course Fuzzy logic Fuzzy AGV Summary References Color Blending – Self-Organizing Maps and Elastic Neural Networks Under the hood of an SOM Summary Facial and Motion Detection – Imaging Filters Facial detection Motion detection Adding detection to your application Summary Encyclopedias and Neurons – Traveling Salesman Problem Traveling salesman problem Learning rate parameter Learning radius Summary Should I Take the Job – Decision Trees in Action Decision tree Decision node Decision variable Decision branch node collection Should I take the job? numl Accord.NET decision trees Learning code Confusion matrix True positives True negatives False positives False negatives Recall Precision Error type visualization Summary References Deep Belief – Deep Networks and Dreaming Restricted Boltzmann Machines Layering What does a computer dream? Summary References Microbenchmarking and Activation Functions Visual activation function plotting Plotting all functions The main Plot function Benchmarking Summary Intuitive Deep Learning in C# .NET What is deep learning? OpenCL OpenCL hierarchy Compute kernel Compute program Compute sampler Compute device Compute resource Compute object Compute context Compute command queue Compute buffer Compute event Compute image Compute platform Compute user event The Kelp.Net Framework Functions Function stacks Function dictionaries Caffe1 Chainer Loss Model saving and loading Optimizers Datasets CIFAR CIFAR-10 CIFAR-100 MNIST Tests Monitoring Kelp.Net Watches Messages Properties Weaver Writing tests Benchmarking functions Running a Single Benchmark Summary References Quantum Computing – The Future Superposition Teleportation Entanglement CNOT H M Summary