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Advanced Machine Learning with ENCOG - Part 2

Course Summary

In this course, you will learn to implement dimensionality reduction and clustering using self-organizing maps, pattern recall and reconstruction using Hopfield networks, time series forecasting using temporal dataset, and optimization using genetic algorithm. This course will not only provide you fundamental knowledge of aforementioned topics, but also will help you to implement these applications using ENCOG machine learning framework.


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    Course Syllabus

    Course Introduction
    - 6m 1s

    —Introduction 2m 10s
    —Course Scope 2m 46s
    —Course Structure 1m 4s
    Unsupervised Competitive Networks
    - 1h 11m

    —Introduction 1m 4s
    —Outline 1m 10s
    —Unsupervised Learning 2m 59s
    —Unsupervised Learning Tasks 6m 0s
    —Unsupervised Networks 2m 4s
    —Self-Organizing Map (SOM) Introduction 2m 6s
    —Example Dataset 1m 21s
    —SOM Network Topology 3m 54s
    —SOM Training 7m 35s
    —SOM Training Using ENCOG 6m 15s
    —SOM Demo: Setup SOM Network (XAML Part) 5m 13s
    —SOM Demo: Setup SOM Network (Code Behind) 10m 18s
    —SOM Demo: Training SOM Network 3m 47s
    —SOM Demo: Overlay 5m 12s
    —SOM Demo: Alternative Visualization Approach 8m 24s
    —Applications of SOM 2m 10s
    —Other Unsupervised Learning Techniques 0m 50s
    —Summary 1m 18s
    Unsupervised Auto-Associative Networks
    - 27m 31s
    Case Study: Time Series Forecasting
    - 45m 1s
    Optimization Using Genetic Algorithm
    - 1h 5m
    Course Summary
    - 6m 20s


Course Fee:
USD 29

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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