Artificial Neural Networks (ANN)

These terms encapsulate the fundamental concepts, structures, and variations within the field of artificial neural networks, highlighting the key components and types of ANNs that are instrumental in modern AI and machine learning.

Activation Function - A function in a neural network node that determines the output of that node given an input or set of inputs.

Artificial Neural Network (ANN) - A computational model inspired by the structure and function of biological neural networks, used in machine learning.

Autoencoder - A type of neural network used for unsupervised learning tasks, such as dimensionality reduction or feature learning, by learning to encode input into a (typically) lower-dimensional space and then decode it back to the original form.

Convolutional Neural Network (CNN) - A class of deep neural networks, most commonly applied to analyzing visual imagery, characterized by their use of convolutional layers.

Deep Learning (DL) - A subset of machine learning involving neural networks with multiple layers that learn representations of data with multiple levels of abstraction.

Hidden Layer - Layers of nodes between the input layer and output layer in a neural network that are not directly exposed to the input.

Input Layer - The first layer in a neural network that receives the input signal to be processed by subsequent layers of neurons.

Neuron - A node in an artificial neural network that simulates the neurons in a biological brain, processing and transmitting information.

Output Layer - The final layer in a neural network where the ultimate output is derived from the processed inputs.

Recurrent Neural Network (RNN) - A type of neural network where connections between nodes form a directed graph along a temporal sequence, which allows it to exhibit temporal dynamic behavior and use its internal state (memory) to process sequences of inputs.

Transformer Architecture - A neural network architecture that relies on self-attention mechanisms and is particularly effective for tasks involving sequential data, such as natural language processing.