Neural networks, deep learning, and natural-language models
Neural networks are a type of machine-learning algorithm that is inspired by the structure and function of the human brain. They are composed of layers of interconnected nodes or artificial neurons that process and transmit information.
In a neural network, the input data is fed into the first layer of nodes, which applies a set of mathematical transformations to the data and produces an output. The output of the first layer is then fed into the second layer, which applies another set of transformations to produce another output, and so on until the final output is produced.
The connections between the nodes in the neural network have weights that are adjusted during the learning process to optimize the network’s ability to make accurate predictions or classifications. This is typically achieved using an optimization algorithm such as stochastic gradient descent. An example of the structure of a neural network...