Advanced Machine Learning with ENCOG

By Abhishek Kumar
In this course you will learn advanced topics related to machine learning for more accurate neural network predictive models. You will also learn different types of neural networks and their implementations using open source machine learning framework ENCOG.
Course info
Rating
(100)
Level
Advanced
Updated
Nov 27, 2013
Duration
4h 10m
Table of contents
Course Introduction
Introduction 3m Course Scope 2m Course Structure 2m Quick Recap 4m
Network Tuning - Part 1
Introduction 1m Outline 1m Network Tuning 3m Underfitting And Overfitting 5m Selection of Layers and Neurons 5m Why Network Pruning? 3m About Pruning 1m ENCOG Support for Pruning 3m Training, Cross Validation and Test Dataset 2m Demo Introduction 3m Demo: XAML Code 9m Demo: Core Steps-Shuffle, Segregate, Normalize and Prune 14m Demo: Core Steps-Train 11m Demo: Observations 8m Summary 2m
Network Tuning - Part 2
Introduction 1m Outline 0m Training Process Tuning 2m ENCOG Training Strategies 2m Greedy Strategy 1m Demo: Greedy Strategy 3m Hybrid Strategy 2m Demo: Hybrid Strategy 7m Reset Strategy 1m Demo: Reset Strategy 2m Required Improvement Strategy 3m Demo: Required Improvement Strategy 2m Smart Learning Rate and Smart Momentum Strategy 1m Demo: Smart Learning Rate and Smart Momentum Strategy 1m StopTrainingStrategy 2m Demo: Basic Stop Strategies 3m Demo: StopTrainingStrategy 1m EarlyStoppingStrategy 2m Demo: EarlyStoppingStrategy 1m Summary 2m
Neural Network Architectures Overview
Introduction 0m Outline 0m Type of Network Covered 2m Why So Many? 4m Architectural Tree 6m Summary 1m
Feed Forward Network - Part 1
Introduction 1m Outline 1m Feed Forward Networks 3m Input Output Mapping 1m Linear Versus Non-Linear 9m Linear Neural Networks 1m Adaline Network 1m Adaline Network in ENCOG 2m Demo: Adaline Network 11m Perceptron Network 1m Perceptron Network in ENCOG 2m Demo: Perceptron Network 2m Summary 1m
Feed Forward Network - Part 2
Introduction 1m Outline 1m Non-Linear Neural Networks 2m Multi Layer Perceptron 2m Multi Layer Perceptron in ENCOG 4m Demo: MLP Network 3m RBF (Radial Basis Function) Network 5m Radial Basis Function Calculation 4m RBF Network Implementation 5m XOR Problem using RBF 5m Basic RBF Network Implementation 2m RBF Network in ENCOG 5m Demo: RBF Network 3m Applications of Feed Forward Networks 1m Summary 1m
Feedback Networks
Introduction 1m Outline 1m Feedback Networks 4m Elman Network 4m Temporal XOR Problem 2m Elman Network Training 1m Elman Network in ENCOG 2m Demo: Elman Network 8m Jordan Network 2m Jordan Network in ENCOG 1m Demo: Jordan Network 1m Applications of Recurrent Networks 1m Summary 1m
Course Summary
Introduction 0m Summary 6m Next Course Glimpse 1m
Description
Course info
Rating
(100)
Level
Advanced
Updated
Nov 27, 2013
Duration
4h 10m
Description

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 introductory course, "Introduction to Machine Learning with ENCOG 3," laid out a solid foundation of machine learning and neural networks. This course will build upon that foundation for more advanced machine learning implementations. In this course, you will learn about various neural network optimization techniques to overcome the problems of underfitting and overfitting and to create more accurate predictive models. This course will also provide an overall picture of various neural network architectures and reasons for their existence. This course will be focused towards implementation of various supervised feed forward and feedback networks. During the whole course, we will be using open source machine learning framework ENCOG to implement various concepts discussed in this course. Although the implementations in this course are ENCOG-based, concepts discussed in this course are widely applicable in other frameworks or even in custom development.

About the author
Abhishek Kumar
About the author

Abhishek Kumar is a data science consultant, author, and Google Developers Expert (GDE) in machine learning. He holds a master’s degree from the University of California, Berkeley, and has been featured in the "Top 40 under 40 Data Scientist" list.

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