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Copyright and Credits
by Matt R. Cole
Hands-On Machine Learning with C#
Title Page
Copyright and Credits
Hands-On Machine Learning with C#
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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
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