2013
Pluralsight
Abhishek Kumar
4h 11m
English

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.

01. Course Introduction
01. Introduction.wmv
02. Course Scope.wmv
03. Course Structure.wmv
04. Quick Recap.wmv
05. Summary.wmv

02. Network Tuning - Part 1
01. Introduction.wmv
02. Outline.wmv
03. Network Tuning.wmv
04. Underfitting And Overfitting.wmv
05. Selection of Layers and Neurons.wmv
06. Why Network Pruning .wmv
07. About Pruning.wmv
08. ENCOG Support for Pruning.wmv
09. Training, Cross Validation and Test Dataset.wmv
10. Demo Introduction.wmv
11. Demo XAML Code.wmv
12. Demo Core Steps-Shuffle, Segregate, Normalize and Prune.wmv
13. Demo Core Steps-Train.wmv
14. Demo Observations.wmv
15. Summary.wmv

03. Network Tuning - Part 2
01. Introduction.wmv
02. Outline.wmv
03. Training Process Tuning.wmv
04. ENCOG Training Strategies.wmv
05. Greedy Strategy.wmv
06. Demo Greedy Strategy.wmv
07. Hybrid Strategy.wmv
08. Demo Hybrid Strategy.wmv
09. Reset Strategy.wmv
10. Demo Reset Strategy.wmv
11. Required Improvement Strategy.wmv
12. Demo Required Improvement Strategy.wmv
13. Smart Learning Rate and Smart Momentum Strategy.wmv
14. Demo Smart Learning Rate and Smart Momentum Strategy.wmv
15. StopTrainingStrategy.wmv
16. Demo Basic Stop Strategies.wmv
17. Demo StopTrainingStrategy.wmv
18. EarlyStoppingStrategy.wmv
19. Demo EarlyStoppingStrategy.wmv
20. Summary.wmv

04. Neural Network Architectures Overview
01. Introduction.wmv
02. Outline.wmv
03. Type of Network Covered.wmv
04. Why So Many .wmv
05. Architectural Tree.wmv
06. Summary.wmv

05. Feed Forward Network - Part 1
01. Introduction.wmv
02. Outline.wmv
03. Feed Forward Networks.wmv
04. Input Output Mapping.wmv
05. Linear Versus Non-Linear.wmv
06. Linear Neural Networks.wmv
07. Adaline Network.wmv
08. Adaline Network in ENCOG.wmv
09. Demo Adaline Network.wmv
10. Perceptron Network.wmv
11. Perceptron Network in ENCOG.wmv
12. Demo Perceptron Network.wmv
13. Summary.wmv

06. Feed Forward Network - Part 2
01. Introduction.wmv
02. Outline.wmv
03. Non-Linear Neural Networks.wmv
04. Multi Layer Perceptron.wmv
05. Multi Layer Perceptron in ENCOG.wmv
06. Demo MLP Network.wmv
07. RBF (Radial Basis Function) Network.wmv
08. Radial Basis Function Calculation.wmv
09. RBF Network Implementation.wmv
10. XOR Problem using RBF.wmv
11. Basic RBF Network Implementation.wmv
12. RBF Network in ENCOG.wmv
13. Demo RBF Network.wmv
14. Applications of Feed Forward Networks.wmv
15. Summary.wmv

07. Feedback Networks
01. Introduction.wmv
02. Outline.wmv
03. Feedback Networks.wmv
04. Elman Network.wmv
05. Temporal XOR Problem.wmv
06. Elman Network Training.wmv
07. Elman Network in ENCOG.wmv
08. Demo Elman Network.wmv
09. Jordan Network.wmv
10. Jordan Network in ENCOG.wmv
11. Demo Jordan Network.wmv
12. Applications of Recurrent Networks.wmv
13. Summary .wmv

08. Course Summary
01. Introduction.wmv
02. Summary.wmv
03. Next Course Glimpse.wmv
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