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.
What you'll learn
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 Advanced Machine Learning tutorial, you will learn:
- The various neural network optimization techniques to overcome the problems of underfitting and overfitting
- How to create more accurate predictive models with ENCOG
- You will also see the overall picture of various neural network architectures and reasons for their existence
- Learn 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.
Table of contents
- 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
- 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
- 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
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. He is also a public speaker and has delivered talks in top data conferences across the globe including Strata Data, AI conference, ODSC, and Fifth Elephant. His focus area is machine learning and deep learning at scale and is also a recipient of the... more
