Introduction to Machine Learning with ENCOG 3
This course is focused on implementation and applications of various machine learning methods.
What you'll learn
This course is focused on implementation and applications of various machine learning methods. As machine learning is a very vast area, this course will be targeted more towards one of the machine learning methods which is neural networks. The course will try to make a base foundation first by explaining machine learning through some real world applications and various associated components. In this course, we'll take one of the open source machine learning framework for .NET, which is ENCOG. The course will explain how ENCOG fits into the picture for machine learning programming. Then we'll learn to create various neural network components using ENCOG and how to combine these components for real world scenarios. We'll go in detail of feed forward networks and various propagation training methodologies supported in ENCOG. We'll also talk about data preparation for neural networks using normalization process. Finally, we will take a few more case studies and will try to implement tasks of classification & regression. In the course I will also give some tips & tricks for effective & quick implementations of neural networks in real world applications.
Table of contents
- Introduction 1m
- Outline 1m
- Human Neuron vs Artificial Neuron 2m
- Neuron Computation 1m
- Neural Network Component : Neuron Types 2m
- Neural Network Component : Weights 1m
- Neural Network Component : Activation Function 1m
- Neural Network Component : Layers 2m
- Neural Network Computation 3m
- Model Creation 3m
- Model Training 4m
- Model Validation 1m
- Summary 1m
- Introduction 0m
- Outline 1m
- Case Study 1: Classification Task 2m
- Flow Chart 2m
- Demo : Case Study 1 : Shuffle 3m
- Demo : Case Study 1 : Segregate 2m
- Demo : Case Study 1 : Normalize 4m
- Demo : Case Study 1 : Create Network 3m
- Demo : Case Study 1 : Train Network 2m
- Demo : Case Study 1 : Evaluate Network 4m
- Case Study 2: Regression Task 2m
- Flow Chart 2m
- Demo : Case Study 2 : Shuffle 2m
- Demo : Case Study 2 : Segregate 1m
- Demo : Case Study 2 : Normalize 3m
- Demo : Case Study 2 : Create Network 1m
- Demo : Case Study 2 : Train Network 1m
- Demo : Case Study 2 : Evaluate Network 3m
- Summary 1m