Introduction
Learn what neural networks are and how they model the way the brain works.
📚 Programming Tutorial
Neural Networks
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).
NN Tutorial Index
13 Topics📘 Basics
Basic models
Learn the basic models of artificial neural networks and how they are built.
Terminologies
Learn the key terms used in neural networks, such as weights, bias, activation function and threshold.
🎓 Supervised Learning Networks
Perceptron Networks
Learn single-layer and multiple-layer perceptron algorithms and the weight update rule.
Adaptive Linear Neuron
Learn Adaline and Madaline networks and their learning algorithms.
Back Propagation Networks
Learn the multilayer feed-forward network and its three-phase training algorithm.
Associate Memory Network
Learn auto associative and hetero associative memory networks.
Bidirectional Associative Memory
Learn BAM, a recurrent hetero associative network for pattern pairs.
Hopfield Networks
Learn discrete and continuous Hopfield networks.
🧭 Unsupervised Learning Networks
Fixed Weight Competitive Networks
Learn competitive networks such as Maxnet with fixed weights.
Kohonen Self-Organizing Feature Maps
Learn how self-organizing maps cluster and map input patterns.
Learning Vector Quantization
Learn LVQ, a supervised variant of vector quantization.
Counter Propagation Networks
Learn full and forward-only counter propagation networks.
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