A neural network is a type of Machine Learning algorithm that is inspired by the structure and function of the human brain. It consists of a network of interconnected processing nodes, called neurons, that work together to learn patterns and make predictions from data.
Each neuron in a neural network receives input from other neurons, applies a mathematical transformation to that input, and passes the output to other neurons in the network. The output of the last layer of neurons is the final output of the network, which can be a prediction or a decision based on the input data.
The strength of the connections between neurons, called weights, is learned during the training phase of the neural network. This is done by adjusting the weights to minimize the difference between the predicted output of the network and the actual output.
Neural networks can be used for a wide range of tasks, including image and speech recognition, natural language processing, and predictive analytics. Some popular types of neural networks include feedforward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders.
Neural networks have become a key component of many machine learning systems and have contributed to significant advances in fields such as computer vision, speech recognition, and natural language processing.