Artificial Neural Networks

Artificial neural network An artificial neural network is a system implemented on a computer system and roughly simulates the learning function and memory function of the human brain as special hardware or complex software. They try to simulate multilayer processing elements in the brain called neurons. These elements are implemented so that layers can learn from previous experiences and remember their output. In this way, the system can learn to identify specific patterns and situations, apply them to specific priorities, and output appropriate results.

In this course, you will understand the intuition behind the artificial neural network, apply artificial neural networks to practice, understand the intuition behind the convolution neural network, actually apply the convolution neural network, I understand the background. Intuition, actually the application of recurrent neural network understands the intuition behind the self-organizing map, actually applies self-organizing mapping, understands the intuition behind the Boltzmann machine, and actually the Boltzmann machine Apply and understand the intuition behind the auto encoder

The construction of an artificial neural network is very similar to its natural one. It is designed as an information processing system with common features of natural neural networks. An artificial neural network contains a number of processing units called cells, nodes, or neurons. These units are connected to one another by communication links with associated weights. The weights associated with this connection can be adjusted so that the neural network can process the information correctly. It is like synaptic adaptation. Each neuron in the network has an activation function or activity level that determines whether it is necessary to pass the signal received at one end of the neuron to the other end. This can be regarded as the tolerance of neurons. This means that the sum of the input multiplied by the weight of the corresponding connection needs to exceed the tolerance to pass the signal to the next neuron.