Advanced Machine Learning with ENCOG
Pluralsight
Course Summary
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 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.
-
+
Course Syllabus
Course Introduction- 12m 23s
—Introduction 3m 16s
—Course Scope 1m 41s
—Course Structure 2m 22s
—Quick Recap 4m 18s
—Summary 0m 46sNetwork Tuning - Part 1- 1h 11m
—Introduction 1m 25s
—Outline 0m 56s
—Network Tuning 2m 39s
—Underfitting And Overfitting 5m 7s
—Selection of Layers and Neurons 5m 9s
—Why Network Pruning? 2m 49s
—About Pruning 1m 2s
—ENCOG Support for Pruning 3m 25s
—Training, Cross Validation and Test Dataset 2m 23s
—Demo Introduction 3m 16s
—Demo: XAML Code 9m 21s
—Demo: Core Steps-Shuffle, Segregate, Normalize and Prune 13m 31s
—Demo: Core Steps-Train 11m 13s
—Demo: Observations 7m 47s
—Summary 1m 37sNetwork Tuning - Part 2- 40m 32sNeural Network Architectures Overview- 13m 47sFeed Forward Network - Part 1- 34m 17sFeed Forward Network - Part 2- 43m 8sFeedback Networks- 28m 41sCourse Summary- 6m 48s