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
- 4.5.1 Visualize the problem of Perceptron
- 4.5.2 Solve the problem using Multi-layer Neural Network
- 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
Implement simple Multi-layer Neural Network
Before solving this problem of Perceptron, let's install Multi-layer Neural Network first, it's based on the knowledge we went through of the previous section such as Forward propagation, Logistic cost function, Backpropagation. Remember our model have 3 layers: 1 input layer, 1 hidden layer and 1 output layer. ### Now, let’s see the error curve of Multi-layer
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