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Advanced Machine Learning with ENCOG - Part 2

Course Summary

Finding patterns in a multidimensional dataset has always been a challenging task, but self-organizing maps can simplify this process and can help to find interesting patterns and inferences. In this course, you will learn not only the fundamentals of sel


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    Course Syllabus

    ● Course Introduction
        ◦ Introduction
        ◦ Course Scope
        ◦ Course Structure
    ● Unsupervised Competitive Networks
        ◦ Introduction
        ◦ Outline
        ◦ Unsupervised Learning
        ◦ Unsupervised Learning Tasks
        ◦ Unsupervised Networks
        ◦ Self-Organizing Map (SOM) Introduction
        ◦ Example Dataset
        ◦ SOM Network Topology
        ◦ SOM Training
        ◦ SOM Training Using ENCOG
        ◦ SOM Demo: Setup SOM Network (XAML Part)
        ◦ SOM Demo: Setup SOM Network (Code Behind)
        ◦ SOM Demo: Training SOM Network
        ◦ SOM Demo: Overlay
        ◦ SOM Demo: Alternative Visualization Approach
        ◦ Applications of SOM
        ◦ Other Unsupervised Learning Techniques
        ◦ Summary
    ● Unsupervised Auto-Associative Networks
        ◦ Introduction
        ◦ Outline
        ◦ Associative Memory
        ◦ Types of Associative Memory
        ◦ Hopfield Network Introduction
        ◦ Hopfield Network Working
        ◦ Hopfield Network in ENCOG
        ◦ Demo: Memory Recall and Reconstruction
        ◦ Applications of Hopfield Network
        ◦ Summary
    ● Case Study: Time Series Forecasting
        ◦ Introduction
        ◦ Outline
        ◦ Time Series Forecasting
        ◦ Time Series Components
        ◦ Forecasting Window
        ◦ Forecasting Using Neural Networks
        ◦ Forecasting Using ENCOG Framework: TemporalMLDataSet
        ◦ Forecasting Using ENCOG Framework: TemporalDataDescription
        ◦ Forecasting Using ENCOG Framework: TemporalPoint
        ◦ Case Study: Time Series Forecasting
        ◦ Demo: Time Series Forecasting - Introduction
        ◦ Demo: Step 1 - Read Data
        ◦ Demo: Step 2 - Normalize Data
        ◦ Demo: Step 3 - Generate Temporal Data
        ◦ Demo: Step 4 - Create and Train Neural Network
        ◦ Demo: Step 5 - Evaluate Neural Network
        ◦ Summary
    ● Optimization Using Genetic Algorithm
        ◦ Introduction
        ◦ Outline
        ◦ Non-Propagation Supervised Learning
        ◦ Optimization Problems
        ◦ Search Space
        ◦ Genetic Algorithm
        ◦ Genetic Algorithm Flowchart
        ◦ Population
        ◦ Encoding
        ◦ Fitness Function
        ◦ Selection
        ◦ Crossover
        ◦ Mutation
        ◦ Encoding Genome Using ENCOG
        ◦ Population Using ENCOG
        ◦ Fitness Function Using ENCOG
        ◦ Genetic Trainer Using ENCOG
        ◦ Crossover Operators Using ENCOG
        ◦ Mutation Operators Using ENCOG
        ◦ Genetic Algorithm Training Using ENCOG
        ◦ Demo: Traveling Salesman Problem - Setup
        ◦ Demo: Traveling Salesman Problem - Population
        ◦ Demo: Traveling Salesman Problem - Fitness Function
        ◦ Demo: Traveling Salesman Problem - Genetic Trainer
        ◦ Demo: Traveling Salesman Problem - Iterations
        ◦ Applications of Genetic Algorithm
        ◦ Summary
    ● Course Summary
        ◦ Course Summary
        ◦ References and Resources
        ◦ Future Roadmap

     


Course Fee:
USD 29

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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