Deep Learning

Deep Learning is a subfield of machine learning that uses artificial neural networks to solve complex problems such as image recognition, speech recognition, and natural language processing. Artificial neural networks are algorithms inspired by the structure and function of the human brain, and they can be trained to recognize patterns and make decisions based on input data.

FFNN

Feedforward Neural Networks (FFNN), also known as Multi-layer Perceptrons (MLPs), are the simplest type of neural networks. They consist of an input layer, some hidden layers, and an output layer, and the information flows only in one direction from the input layer to the output layer. FFNNs are widely used for tasks such as regression and classification.

CNN

Convolutional Neural Networks (CNN) are characterized by the presence of Convolutional layers, used to extract features from the input data, pooling layers to reduce the spatial resolution of the data, and fully-connected layers to produce the final output. CNNs have been very successful in computer vision tasks, such as object recognition and image classification.

RNN

Recurrent Neural Networks (RNN) are a type of neural network designed to process sequences of data, thus they're characterized by 'memory': they use a recurrent connection to allow information from previous time steps to be used in the processing of the current time step. RNNs can be used for sequential tasks such as language translation, sentiment analysis, and speech recognition.