In our first task, we are going to learn and implement a basic neural network - "perceptron". In machine learning, the perceptron is an algorithm for supervised learning. It is a type of linear classifier which can classify linearly separable data. The following links give you a starting point to study perceptron. You are more than welcome to share any resource you found online with us.
Tutorials:
- How the Perceptron Algorithm Works 1/2
- How the Perceptron Algorithm Works 2/2
- Java Implementation of the Perceptron Algorithm
- Understanding Multi-Layer Perceptron (MLP) .. How it Works
- Artificial Neural Networks (Part 1) - Classification using Single Layer Perceptron Model
- Artificial Neural Networks (Part 2) - -Classification using Multi-Layer Perceptron Model
- Artificial Neural Networks (Part 3) - Backpropagation
- Perceptrons for Dummies
Goals:
- Understand the basic operation of Perceptron
- Backpropagation
- Learning rules of Perceptron
- Limitations of Perceptron
- Implement a Single-Layer Perceptron (SLP)
- Real-world applications of Perceptron
- Implement a Multi-Layer Perceptron (MLP)
Target Dates:
- Study Phase: 12/10/2015 - 12/23/2015
- Implementation Phase: 12/24/2015 - 01/03/2015
- Seminar: 01/08/2015
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