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Advanced Machine Learning with ENCOG

Course Summary

Are you worried about your neural network model prediction accuracy? Are you not sure about your neural network model selection for your machine learning problem? This course will introduce you to more advanced topics in machine learning. The previous int


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    Course Syllabus

    ● Course Introduction
        ◦ Introduction
        ◦ Course Scope
        ◦ Course Structure
        ◦ Quick Recap
        ◦ Summary
    ● Network Tuning - Part 1
        ◦ Introduction
        ◦ Outline
        ◦ Network Tuning
        ◦ Underfitting And Overfitting
        ◦ Selection of Layers and Neurons
        ◦ Why Network Pruning?
        ◦ About Pruning
        ◦ ENCOG Support for Pruning
        ◦ Training, Cross Validation and Test Dataset
        ◦ Demo Introduction
        ◦ Demo: XAML Code
        ◦ Demo: Core Steps-Shuffle, Segregate, Normalize and Prune
        ◦ Demo: Core Steps-Train
        ◦ Demo: Observations
        ◦ Summary
    ● Network Tuning - Part 2
        ◦ Introduction
        ◦ Outline
        ◦ Training Process Tuning
        ◦ ENCOG Training Strategies
        ◦ Greedy Strategy
        ◦ Demo: Greedy Strategy
        ◦ Hybrid Strategy
        ◦ Demo: Hybrid Strategy
        ◦ Reset Strategy
        ◦ Demo: Reset Strategy
        ◦ Required Improvement Strategy
        ◦ Demo: Required Improvement Strategy
        ◦ Smart Learning Rate and Smart Momentum Strategy
        ◦ Demo: Smart Learning Rate and Smart Momentum Strategy
        ◦ StopTrainingStrategy
        ◦ Demo: Basic Stop Strategies
        ◦ Demo: StopTrainingStrategy
        ◦ EarlyStoppingStrategy
        ◦ Demo: EarlyStoppingStrategy
        ◦ Summary
    ● Neural Network Architectures Overview
        ◦ Introduction
        ◦ Outline
        ◦ Type of Network Covered
        ◦ Why So Many?
        ◦ Architectural Tree
        ◦ Summary
    ● Feed Forward Network - Part 1
        ◦ Introduction
        ◦ Outline
        ◦ Feed Forward Networks
        ◦ Input Output Mapping
        ◦ Linear Versus Non-Linear
        ◦ Linear Neural Networks
        ◦ Adaline Network
        ◦ Adaline Network in ENCOG
        ◦ Demo: Adaline Network
        ◦ Perceptron Network
        ◦ Perceptron Network in ENCOG
        ◦ Demo: Perceptron Network
        ◦ Summary
    ● Feed Forward Network - Part 2
        ◦ Introduction
        ◦ Outline
        ◦ Non-Linear Neural Networks
        ◦ Multi Layer Perceptron
        ◦ Multi Layer Perceptron in ENCOG
        ◦ Demo: MLP Network
        ◦ RBF (Radial Basis Function) Network
        ◦ Radial Basis Function Calculation
        ◦ RBF Network Implementation
        ◦ XOR Problem using RBF
        ◦ Basic RBF Network Implementation
        ◦ RBF Network in ENCOG
        ◦ Demo: RBF Network
        ◦ Applications of Feed Forward Networks
        ◦ Summary
    ● Feedback Networks
        ◦ Introduction
        ◦ Outline
        ◦ Feedback Networks
        ◦ Elman Network
        ◦ Temporal XOR Problem
        ◦ Elman Network Training
        ◦ Elman Network in ENCOG
        ◦ Demo: Elman Network
        ◦ Jordan Network
        ◦ Jordan Network in ENCOG
        ◦ Demo: Jordan Network
        ◦ Applications of Recurrent Networks
        ◦ Summary
    ● Course Summary
        ◦ Introduction
        ◦ Summary
        ◦ Next Course Glimpse

     


Course Fee:
USD 29

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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