Description
Part One. Getting Started with Neural NetworksChapter 1. Learning Neural Network Chapter Goal: This chapter introduces you with the Artificial Intelligence Neural Networks
Sub-Topics
Biological and artificial neurons Activation functions Summary
Chapter 2. Internal Mechanism of Neural Network ProcessingChapter Goal: The chapter explores the inner machinery of neural network processing
Sub-Topics
Function to be approximatedNetwork architecture Forward pass calculations Back-propagation pass calculationsFunction derivative and function divergent Table of most commonly used function derivativesSummary
Chapter 3. Manual Neural Network Processing Chapter Goal: Manual neural network processing
Sub-Topics
Example 1. Manual approximation of a function at a single point Building the neural network Forward pass calculation Backward pass calculation Calculating weight adjustments for the output layer neurons Calculating weight adjustments for the hidden layer neuronsUpdating network biases Back to the forward passMatrix form of network calculationDigging deeper Mini-batches and stochastic gradient Summary
Part Two. Neural Network Java Development Environment Chapter 4. Configuring Your Development Environment Chapter Goal: Explain how to download and install a set of tools necessary for building, debugging, testing, and executing neural network applications.
Sub-Topics
Installing Java 8 environment on your Windows machineInstalling NetBeans IDEInstalling Encog Java framework Installing XChart Package Summary
Chapter 5. Neural Network Development Using Java EncogFramework Chapter Goal: Using Java Encog framework.
Sub-Topics
Example 2. Function approximation using Java environmentNetwork architecture Normalizing the input datasets Building the Java program that normalizes both datasetsProgram code Debugging and executing the program Processing results for the training method Testing the network Testing results Digging deeperSummary
Chapter 6. Neural Network Prediction Outside of the Training Range Chapter Goal: Neural network is not a function extrapolation mechanism.Sub-TopicsExample 3a. Approximating periodic functions outside of the training rangeNetwork architecture for example 3aProgram code for example 3aTesting the networkExample 3b. Correct way of approximating periodic functions outside of the training rangePreparing the training dataNetwork architecture for the example 3bProgram code for example 3bTraining results for example 3bTesting results for example 3b Summary
Chapter 7. Processing Complex Periodic FunctionsChapter Goal: Approximation of the complex periodic functionSub-Topics
Example 4. Approximation of a complex periodic functionData preparation Reflecting function topology in dataNetwork architecture Program codeTesting the network Digging deeperSummary&
Author: Igor Livshin
Publisher: Apress
Published: 01/17/2022
Pages: 414
Binding Type: Paperback
Weight: 2.45lbs
Size: 10.00h x 7.00w x 1.31d
ISBN13: 9781484273678
ISBN10: 1484273672
BISAC Categories:
- Computers | Artificial Intelligence | General
- Computers | Languages | Java
- Computers | Programming | Open Source
Sub-Topics
Biological and artificial neurons Activation functions Summary
Chapter 2. Internal Mechanism of Neural Network ProcessingChapter Goal: The chapter explores the inner machinery of neural network processing
Sub-Topics
Function to be approximatedNetwork architecture Forward pass calculations Back-propagation pass calculationsFunction derivative and function divergent Table of most commonly used function derivativesSummary
Chapter 3. Manual Neural Network Processing Chapter Goal: Manual neural network processing
Sub-Topics
Example 1. Manual approximation of a function at a single point Building the neural network Forward pass calculation Backward pass calculation Calculating weight adjustments for the output layer neurons Calculating weight adjustments for the hidden layer neuronsUpdating network biases Back to the forward passMatrix form of network calculationDigging deeper Mini-batches and stochastic gradient Summary
Part Two. Neural Network Java Development Environment Chapter 4. Configuring Your Development Environment Chapter Goal: Explain how to download and install a set of tools necessary for building, debugging, testing, and executing neural network applications.
Sub-Topics
Installing Java 8 environment on your Windows machineInstalling NetBeans IDEInstalling Encog Java framework Installing XChart Package Summary
Chapter 5. Neural Network Development Using Java EncogFramework Chapter Goal: Using Java Encog framework.
Sub-Topics
Example 2. Function approximation using Java environmentNetwork architecture Normalizing the input datasets Building the Java program that normalizes both datasetsProgram code Debugging and executing the program Processing results for the training method Testing the network Testing results Digging deeperSummary
Chapter 6. Neural Network Prediction Outside of the Training Range Chapter Goal: Neural network is not a function extrapolation mechanism.Sub-TopicsExample 3a. Approximating periodic functions outside of the training rangeNetwork architecture for example 3aProgram code for example 3aTesting the networkExample 3b. Correct way of approximating periodic functions outside of the training rangePreparing the training dataNetwork architecture for the example 3bProgram code for example 3bTraining results for example 3bTesting results for example 3b Summary
Chapter 7. Processing Complex Periodic FunctionsChapter Goal: Approximation of the complex periodic functionSub-Topics
Example 4. Approximation of a complex periodic functionData preparation Reflecting function topology in dataNetwork architecture Program codeTesting the network Digging deeperSummary&
Author: Igor Livshin
Publisher: Apress
Published: 01/17/2022
Pages: 414
Binding Type: Paperback
Weight: 2.45lbs
Size: 10.00h x 7.00w x 1.31d
ISBN13: 9781484273678
ISBN10: 1484273672
BISAC Categories:
- Computers | Artificial Intelligence | General
- Computers | Languages | Java
- Computers | Programming | Open Source
About the Author
Igor Livshin is a senior architect with extensive experience in developing large-scale applications. He worked for many years for two large insurance companies: CNN and Blue Cross & Blue Shield of Illinois. He currently works as a senior researcher at DevTechnologies, specializing in AI and neural networks. Igor has a master's degree in computer science from the Institute of Technology in Odessa, Russia/Ukraine.