Advanced Machine Learning with ENCOG

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
(95)
Level
Advanced
Updated
November 27, 2013
Duration
4h 11m
Table of contents
Course Introduction
Network Tuning - Part 1
Network Tuning - Part 2
Neural Network Architectures Overview
Feed Forward Network - Part 1
Feed Forward Network - Part 2
Feedback Networks
Course Summary
Description
Course info
Rating
(95)
Level
Advanced
Updated
November 27, 2013
Duration
4h 11m
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
About the author

Abhishek Kumar is a data science consultant, author and speaker.He holds Master’s degree from University of California, Berkeley.His focus area is machine learning & deep learning at scale.

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