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Fundamentals of Neural Networks and NeuroSolutions
DAY 2:
Fundamentals of Adaptive
Systems and Neural Networks
- Adaptive Systems and Linear
Regression
- Analyzing linear adaptive systems
- Understanding gradient descent
training
Supervised Learning
- Overview of MLPs (nonlinear
extenstions to linear adaptive systems)
- Tips and tricks of the trade: MLP
parameters and how to set them
- Applications of MLPs
- Genetic optimization of parameters
- Project 1: Using MLPs for
classification
Unsupervised Learning
- Intro to unsupervised learning
- Hebbian learning and principal
component analysis
- Competitive learning and
clustering (including SOMs)
DAY 3:
Radial basis functions (RBFs)
- Introduction to unsupervised
learning
- What are RBFs and why/when should
you use them?
- How to use RBFs and how to set
their parameters
- Hybrid unsupervised/supervised
networks
- Project 2: Using a hybrid RBF/MLP
for classification
Temporal processing and
dynamical systems
- Adaptive signal processing
fundamentals
- Temporal neural networks
DAY 4:
Advanced Genetic Optimization
- Optimizing inputs, learning rates, network size, etc.
Financial Forecasting using Neural Networks
- Introduction to prediction and the stock market
- Optimal trading signals
- Neural network prediction
- Building a trading system
- Analyzing and optimizing the trading system
Overview of using the advanced features and capabilities of NeuroSolutions
- Using Macros to automate tasks
- Introduction to macros and the MacroWizard utility
- Recording a sequence of events
- Using the MacroWizard editor and debugger
- Assigning macros to dialog components and toolbar buttons
- Customizing components using DLLs
- Creating a new processing element activation function
- Updating the backpropagation plane
- Creating a new error criteria
- Creating a new gradient search component
- Specialized I/O
- Creating a new file translator
- Reading/Writing data to/from an external source
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