ML0041 Concept of NN

A neural network (NN) is a machine learning model composed of layers of interconnected neurons. It learns patterns in data by adjusting weights through training, enabling it to perform tasks like classification, regression, and more.

(1) Inspired by Biology: Neural networks are computer systems modeled after the human brain’s network of neurons.
(2) Layered Structure: Neural networks consist of an input layer, one or more hidden layers, and an output layer.
(3) Neurons and Activation: Each neuron performs a weighted sum of its inputs, adds a bias, and applies an activation function to produce an output. Weights and Biases are learnable parameters adjusted during training. Activation Functions can introduce non-linearity (e.g., ReLU, Sigmoid).
(4) Learning Process: Neural networks learn by adjusting the weights and biases through training algorithms such as backpropagation, minimizing errors between predictions and actual results.
(5) Versatility in Applications: Neural networks can identify complex patterns, making them suitable for tasks like image recognition, natural language processing, and data classification.

Below shows an example of an NN.