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Neural Networks in Javascript

#artificialintelligence

Neural networks provide the possibility to solve complicated non linear problems. They can be used in various areas such as signal classification, forecasting timeseries and pattern recognition. A neural network is a model inspired by the human brain and consists of multiple connected neurons. For getting a deeper understanding, I recommend checking out Neural Networks and Deep Learning. Within the last years, multiple Javascript frameworks were developed that can help you to create, train and use Neural Networks for different purposes.


What is Neural Networks? Artificial Intelligence Neural Networks

#artificialintelligence

Neural, the word reminds nerves or nervous system. Neural is like a slang language word that points to the nervous systems and its explanatory details. But the machine learning has borrowed this word to develop a new technology Artificial Intelligence (AI), where the neural networks sit as the basic foundation. The nervous system is connected to the brain via the spinal cord. Human brain functions with neurons as its servants to fetch inputs from the sensory organs (nose, eye, ears, skin, tongue), interpret them and send back the perceived information as results.


The Hopfield Model with Multi-Level Neurons

Neural Information Processing Systems

The generalization replaces two state neurons by neurons taking a richer set of values. Two classes of neuron input output relations are developed guaranteeing convergence to stable states. The first is a class of "continuous" relations andthe second is a class of allowed quantization rules for the neurons.


The Hopfield Model with Multi-Level Neurons

Neural Information Processing Systems

The generalization replaces two state neurons by neurons taking a richer set of values. Two classes of neuron input output relations are developed guaranteeing convergence to stable states. The first is a class of "continuous" relations and the second is a class of allowed quantization rules for the neurons.


The Hopfield Model with Multi-Level Neurons

Neural Information Processing Systems

The generalization replaces two state neurons by neurons taking a richer set of values. Two classes of neuron input output relations are developed guaranteeing convergence to stable states. The first is a class of "continuous" relations and the second is a class of allowed quantization rules for the neurons.