- Artificial Neural Network - Home
- Basic Concepts
- Building Blocks
- Learning & Adaptation
- Supervised Learning
- Unsupervised Learning
- Learning Vector Quantization
- Adaptive Resonance Theory
- Kohonen Self-Organizing Feature Maps
- Associate Memory Network
- Hopfield Networks
- Boltzmann Machine
- Brain-State-in-a-Box Network
- Optimization Using Hopfield Network
- Other Optimization Techniques
- Genetic Algorithm
- Applications of Neural Networks
Artificial Neural Networks Tutorial
Artificial Neural Networks are parallel computing devices, which are basically an attempt to make a computer model of the brain. The main objective is to develop a system to perform various computational tasks faster than the traditional systems. This tutorial covers the basic concept and terminologies involved in Artificial Neural Network. Sections of this tutorial also explain the architecture as well as the training algorithm of various networks used in ANN.
Audience
This tutorial will be useful for graduates, post graduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The reader can be a beginner or an advanced learner.
Prerequisites
Artificial Neural Networks (ANN) is an advanced topic, hence the reader must have basic knowledge of Algorithms, Programming, and Mathematics.