1. Introduction
2. History and Overview about Artificial Neural Network
3. Single neural network
7. References
2. History and Overview about Artificial Neural Network
3. Single neural network
- 3.1 Perceptron
- 3.1.1 The Unit Step function
- 3.1.2 The Perceptron rules
- 3.1.3 The bias term
- 3.1.4 Implement Perceptron in Python
- 3.2 Adaptive Linear Neurons
- 3.2.1 Gradient Descent rule (Delta rule)
- 3.2.2 Learning rate in Gradient Descent
- 3.2.3 Implement Adaline in Python to classify Iris data
- 3.2.4 Learning via types of Gradient Descent
- 3.3 Problems with Perceptron (AI Winter)
- 4.1 Overview about Multi-layer Neural Network
- 4.2 Forward Propagation
- 4.3 Cost function
- 4.4 Backpropagation
- 4.5 Implement simple Multi-layer Neural Network to solve the problem of Perceptron
- 4.6 Some optional techniques for Multi-layer Neural Network Optimization
- 4.7 Multi-layer Neural Network for binary/multi classification
- 5.1 Overview about MNIST data
- 5.2 Implement Multi-layer Neural Network
- 5.3 Debugging Neural Network with Gradient Descent Checking
7. References
Overview about Perceptron
We will start from the years Neural Network was born with the name - Perceptron. According to Wikipedia, the Perceptron algorithm was invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt. This is the first simple algorithm for binary classification with one input layer contain many neurons and one output layer with one neuron. Information goes through the Perceptron from input layer, then go to summation functions and finally, through unit step function, the output layer will return value -1 or 1. We will discuss more detail about the unit step function, the perceptron rule, the bias term, respectively.
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