Deep Learning

An activation function is a mathematical operation applied to the output of a neuron in a neural network that decides whether the neuron should 'fire' and pass on a signal.

An autoencoder is a neural network that learns to compress input data into a smaller representation and then reconstruct the original data from that compressed form.

Backpropagation is an algorithm for training neural networks by calculating how much each weight contributed to the prediction error and adjusting those weights accordingly. It uses the chain rule to efficiently compute gradients of the loss function.

A Convolutional Neural Network (CNN) is a specialized type of deep neural network designed to process grid-like data such as images by automatically learning spatial patterns and features.

In deep learning, a decoder is a neural network module that converts an encoded representation (like a context vector or latent features) into a final output such as text, images, or sequences.

Long Short-Term Memory (LSTM) is a type of recurrent neural network architecture designed to learn and retain information over long sequences of data.

A Recurrent Neural Network (RNN) is a type of neural network built to handle sequential data by passing information from one step to the next through a hidden state that acts like a memory.