TUTORIAL- Introduction
- Basic models
- Terminologies
- Supervised Learning Networks
- Perceptron Networks
- Adaptive Linear Neuron
- Back Propagation
- Associate Memory Network
- Bidirectional Associative Memory
- Hopfield Networks
- Unsupervised Learning
- Fixed Weight Competitive Networks
- Kohonen Self-Organizing Feature Maps
- Learning Vector Quantization
- Counter Propagation Networks
Neural Networks Tutorial
Artificial Neural Networks, commonly referred to as "Neural Networks", has been motivated right from its inception by the recognition that the human brain computes in an entirely different way from the conventional digital computer. The brain is a highly complex, nonlinear, and parallel computer (information-processing system). It has the capability to organize its structural constituents, known as neurons, so as to perform certain computations (e.g., pattern recognition, perception, and motor control) many times faster than the fastest digital computer in existence today.
To be specific, the brain routinely accomplishes perceptual recognition tasks (e.g., recognizing familiar face embedded in an unfamiliar scene) in approximately 100–200 ms, whereas tasks of much lesser than the powerful computer.
A neural network is a machine that is designed to model the way in which the brain performs a particular task or function of interest; the network is usually implemented by using electronic components or is simulated in software on a digital computer. To achieve good performance, neural networks employ a massive interconnection of simple computing cells referred to as “neurons” or “processing units.”
TEXT BOOKS:
- Deep Learning By Ian Goodfellow and Yoshua Bengio and Aaron Courville
- Neural Networks and Learning Machines by Simon Haykin, 3rd Edition, Pearson Prentice Hall.
- Principles of Soft Computing by S. N. Sivanandam, 2rd Edition, Wiley.
Tutorial Index :
- Introduction
- Basic models
- Terminologies
- Supervised Learning Networks
- Perceptron Networks
- Adaptive Linear Neuron
- Back Propagation
- Associate Memory Network
- Bidirectional Associative Memory
- Hopfield Networks
- Unsupervised Learning
- Fixed Weight Competitive Networks
- Kohonen Self-Organizing Feature Maps
- Learning Vector Quantization
- Counter Propagation Networks
Next Topic :Introduction to Neural Networks