1. Introduction
2. History and Overview about Artificial Neural Network
7. References
2. History and Overview about Artificial Neural Network
- 2.1 Brief history about Artificial Neural Network
- 2.2 What is Neural Network and how it works
- 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
Brief history about Artificial Neural Networks
I'm just list of important events of the timeline of Artificial Neural Networks when it was born:- 1940s - Neural Networks was born (Simple Logic Gate)
- 1950s - Perceptron (Weights and Threshold)
- 1960s - The first golden age of Neural Networks (Adaline)
- 1970s - The winter of Neural Networks (XOR Problem)
- 1980s - Renew age (Multi-layer Neural Networks and Backpropagation)
- 1990s - Support Vector Machines appearance was promising
- 2010s - Deep Learning had advantages in solving complex problems
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