Neural Networks Overview

Study Notes

Neural networks are modeled after the working of the human brain
Neural networks are made up of layers and each layer is divided into a block that accomplishes its own task
The first layer of neural network is known as input layer and acquires the data
The second layer of neural network is known as hidden layer and all processing occurs in these layers
The third layer of neural network is known as output layer and passes the processed data to the subsequent layer
The fourth layer of neural network is known as the feedback layer and it helps the neural network to learn
The fifth layer of neural network is known as the bias layer and it helps the neural network to generalize
The sixth layer of neural network is known as the pre-processing layer and it helps the neural network to clean the data
The seventh layer of neural network is known as the activation layer and it activates the computing cells
The eighth layer of neural network is known as the weight layer and it assigns a weight to each computing cell
The ninth layer of neural network is known as the bias layer and it helps the neural network to generalize
The tenth layer of neural network is known as the output layer and it passes the processed data to the user
Neural networks are computer programs that can learn to recognize patterns in data.
There are two main types of neural networks: supervised and unsupervised.
Supervised learning involves a teacher or trainer who provides the network with a set of examples of correct answers.
Unsupervised learning involves the network learning to recognize patterns on its own.
Reinforcement learning involves the network learning to find the best possible path in a specific situation.
Neural networks are used in a variety of fields, including machine learning, data mining, and artificial intelligence.