Hands-On Neural Network Programming with C#
Matt R. Cole更新时间：2021-07-23 16:41:20
最新章节：Leave a review - let other readers know what you think封面
Title Page
Copyright and Credits
Hands-On Neural Network Programming with C#
Dedication
Packt Upsell
Why subscribe?
Packt.com
Contributors
About the author
About the reviewers
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
Code in Action
Conventions used
Get in touch
Reviews
A Quick Refresher
Technical requirements
Neural network overview
Neural network training
A visual guide to neural networks
The role of neural networks in today's enterprises
Types of learning
Supervised learning
Unsupervised learning
Reinforcement learning
Understanding perceptrons
Is this useful?
Understanding activation functions
Visual activation function plotting
Function plotting
Understanding back propagation
Forward and back propagation differences
Summary
References
Building Our First Neural Network Together
Technical requirements
Our neural network
Neural network training
Synapses
Neurons
Forward propagation
Sigmoid function
Backward propagation
Calculating errors
Calculating a gradient
Updating weights
Calculating values
Neural network functions
Creating a new network
Importing an existing network
Importing datasets
Testing the network
Exporting the network
Training the network
Testing the network
Computing forward propagation
Exporting the network
Exporting a dataset
The neural network
Neuron connection
Examples
Training to a minimum
Training to a maximum
Summary
Decision Trees and Random Forests
Technical requirements
Decision trees
Decision tree advantages
Decision tree disadvantages
When should we use a decision tree?
Random forests
Random forest advantages
Random forest disadvantages
When should we use a random forest?
SharpLearning
Terminology
Loading and saving models
Example code and applications
Saving a model
Mean squared error regression metric
F1 score
Optimizations
Sample application 1
The code
Sample application 2 – wine quality
The code
Summary
References
Face and Motion Detection
Technical requirements
Facial detection
Motion detection
Code
Summary
Training CNNs Using ConvNetSharp
Technical requirements
Getting acquainted
Filters
Creating a network
Example 1 – a simple example
Example 2 – another simple example
Example 3 – our final simple example
Using the Fluent API
GPU
Fluent training with the MNIST database
Training the network
Testing the data
Predicting data
Computational graphs
Summary
References
Training Autoencoders Using RNNSharp
Technical requirements
What is an autoencoder?
Different types of autoencoder
Standard autoencoder
Variational autoencoders
De-noising autoencoders
Sparse autoencoders
Creating your own autoencoder
Summary
References
Replacing Back Propagation with PSO
Technical requirements
Basic theory
Swarm intelligence
Particle Swarm Optimization
Types of Particle Swarm Optimizations
Original Particle Swarm Optimization strategy
Particle Swarm Optimization search strategy
Particle Swarm Optimization search strategy pseudo-code
Parameter effects on optimization
Replacing back propagation with Particle Swarm Optimization
Summary
Function Optimizations: How and Why
Technical requirements
Getting started
Function minimization and maximization
What is a particle?
Swarm initialization
Chart initialization
State initialization
Controlling randomness
Updating the swarm position
Updating the swarm speed
Main program initialization
Running Particle Swarm Optimization
Our user interface
Run button
Rewind button
Back button
Play button
Pause button
Forward button
Hyperparameters and tuning
Function
Strategy
Dim size
Upper bound
Lower bound
Upper bound speed
Lower bound speed
Decimal places
Swarm size
Max iterations
Inertia
Social weight
Cognitive weight
Inertia weight
Understanding visualizations
Understanding two-dimensional visualizations
Understanding three-dimensional visualizations
Plotting results
Playing back results
Updating the information tree
Adding new optimization functions
The purpose of functions
Adding new functions
Let's add a new function
Summary
Finding Optimal Parameters
Technical requirements
Optimization
What is a fitness function?
Maximization
Gradient-based optimization
Heuristic optimization
Constraints
Boundaries
Penalty functions
General constraints
Constrained optimization phases
Constrained optimization difficulties
Implementation
Meta-optimization
Fitness normalization
Fitness weights for multiple problems
Advice
Constraints and meta-optimization
Meta-meta-optimization
Optimization methods
Choosing an optimizer
Gradient descent (GD)
How it works
Drawbacks
Pattern Search (PS)
How it works
Local Unimodal Sampling (LUS)
How it works
Differential Evolution (DE)
How it works
Particle Swarm Optimization (PSO)
How it works
Many Optimizing Liaisons (MOL)
Mesh (MESH)
Parallelism
Parallelizing the optimization problem
Parallel optimization methods
Necessary parameter tuning
And finally the code
Performing meta-optimization
Computing fitness
Testing custom problems
Base problem
Creating a custom problem
Our Custom Problem
Summary
References
Object Detection with TensorFlowSharp
Technical requirements
Working with Tensors
TensorFlowSharp
Developing your own TensorFlow application
Detecting images
Minimum score for object highlighting
Summary
References
Time Series Prediction and LSTM Using CNTK
Technical requirements
Long short-term memory
LSTM variants
Applications of LSTM
CNTK terminology
Our example
Coding our application
Loading data and graphs
Loading training data
Populating the graphs
Splitting data
Running the application
Training the network
Creating a model
Getting the next data batch
Creating a batch of data
How well do LSTMs perform?
Summary
References
GRUs Compared to LSTMs RNNs and Feedforward networks
Technical requirements
QuickNN
Understanding GRUs
Differences between LSTM and GRU
Using a GRU versus a LSTM
Coding different networks
Coding an LSTM
Coding a GRU
Comparing LSTM GRU Feedforward and RNN operations
Network differences
Summary
Activation Function Timings
Function Optimization Reference
The Currin Exponential function
Description
Input domain
Modifications and alternative forms
The Webster function
Description
Input distributions
The Oakley & O'Hagan function
Description
Input domain
The Grammacy function
Description
Input fomain
Franke's function
Description
Input domain
The Lim function
Description
Input domain
The Ackley function
Description
Input domain
Global minimum
The Bukin function N6
Description
Input domain
Global minimum
The Cross-In-Tray function
Description
Input domain
Global minima
The Drop-Wave function
Description
Input domain
Global minimum
The Eggholder function
Description
Input domain
Global minimum
The Holder Table function
Description
Input domain
Global minimum
The Levy function
Description
Input domain
Global minimum
The Levy function N13
Description
Input domain
Global minimum
The Rastrigin function
Description
Input domain
Global minimum
The Schaffer function N.2
Description
Input domain
Global minimum
The Schaffer function N.4
Description
Input domain
The Shubert function
Description
Input domain
Global minimum
The Rotated Hyper-Ellipsoid function
Description
Input domain
Global minimum
The Sum Squares function
Description
Input domain
Global minimum
The Booth function
Description
Input domain
Global minimum
The Mccormick function
Description
Input domain
Global minimum
The Power Sum function
Description
Input domain
The Three-Hump Camel function
Description
Input domain
Global minimum
The Easom function
Description
Input domain
Global minimum
The Michalewicz function
Description
Input domain
Global minima
The Beale function
Description
Input domain
Global minimum
The Goldstein-Price function
Description
Input domain
Global minimum
The Perm function
Description
Input domain
Global minimum
The Griewank function
Description
Input domain
Global minimum
The Bohachevsky function
Description
Input domain
Global minimum
The Sphere function
Description
Input domain
Global minimum
The Rosenbrock function
Description
Input domain
Global minimum
The Styblinski-Tang function
Description
Input domain
Global minimum
Summary
Keep reading
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更新时间：2021-07-23 16:41:20