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5-Day
Neural Network and NeuroSolutions Course DAY
1:
Introduction to neural networks
- Terminology
- Fundamental principles of neural
networks
- Overview of neural network
architectures and training
- When to use and why should you use
neural networks
Fundamentals of NeuroSolutions
- Overview of breadboards, palettes,
families, etc.
- The Neural Wizard
- Placing and interconnecting
components by hand
- Special focus on how to use
probes, properties, and file components
Using NeuroSolutions
- Creating, training, and testing
neural networks
- Using probes to understand the
training process and the results
- What to look for and how to use
probes in a network
- Using and setting the network
parameters
Using NeuroSolutions for Excel
- Preprocessing and analyzing your
input data
- Tagging your data
- Creating a neural network
- Training a neural network
- Testing a neural network
- Analyzing your results
- Optimizing neural network
parameters / inputs
Overview of other features of
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
DAY 5:
Embedding a Neural Network into your Application
- Using real-time data in NeuroSolutions (OLE automation)
- Introduction to OLE Automation
- Creating C++ code using NeuroSolutions' code generation
- Introduction to C++ Code Generation
- Developing your own application using the Custom Solution Wizard
- Introduction to the Custom Solution Wizard
Selection of advanced topics:
-
Advanced NeuroSolutions for Excel and Batches
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C++ application development with code generation
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Developing an Excel application using the Custom Solution Wizard
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Developing a VB application that communicates with NeuroSolutions through OLE
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Introduction to fuzzy logic and neural networks
-
Introduction to support vector machines
-
Introduction to mixture of experts
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Advanced financial forecasting
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Self-study projects
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