Neural Networks type of machine learning

Neural Networks

 Neural Networks are a type of machine learning algorithm designed to mimic the structure and function of the human brain. They are made up of layers of interconnected nodes (also known as neurons) which are trained on input data to learn patterns and relationships between them.

Neural Networks can be used for a variety of tasks such as image recognition, speech recognition, natural language processing, and even playing games. The process of training a neural network involves feeding it large amounts of labeled data and adjusting the weights and biases of each neuron until the network can accurately predict the correct output for new input data.

There are many different types of neural networks, including feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. Each type is suited for different types of tasks and data.

Neural Networks have become increasingly popular in recent years, thanks in part to advancements in computing power and the availability of large datasets for training. They are used in a wide range of applications, from self-driving cars to virtual assistants, and are expected to play an increasingly important role in the future of technology.