NEURAL NETWORK (Syllabus: GS Paper 3 – Sci and Tech)

Context: Neural networks, inspired by the human brain, revolutionize AI technology.

Neural Network

• About: It is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. 

o It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.

• Composition: They consist of layers of interconnected nodes, or "neurons," which process input data to generate output predictions.
• Neuron Processing: Each neuron receives one or more inputs, applies a weighted sum, and passes the result through an activation function to produce an output.
• Layer Composition: A typical neural network has an input layer, one or more hidden layers, and an output layer.

o The input layer takes in the raw data, while each subsequent hidden layer processes the data further, extracting features and patterns through learned weights and biases.

• Training Mechanism: Training a neural network involves adjusting the weights and biases to minimize the error between the predicted and actual outputs.

o This process is done through an algorithm called backpropagation, which calculates the gradient of the loss function and updates the weights accordingly using an optimization method like gradient descent.

• Application Domains: Neural networks excel in tasks involving large and complex datasets, such as image and speech recognition, natural language processing, and game playing.
• Significance: 

o Medical diagnosis by medical image classification

o Targeted marketing by social network filtering and behavioral data analysis

o Financial predictions by processing historical data of financial instruments

o Electrical load and energy demand forecasting.