Deep Learning

Neural networks, training techniques, and the architectures behind modern AI.

12 articles · 67,424 words

Articles

• Deep learning

  In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning.

• Artificial neural network

  In machine learning, a neural network (NN) or neural net, is a computational model inspired by the structure and functions of biological neural networks.

• Backpropagation

  In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates.

• Convolutional neural network

  A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization.

• Recurrent neural network

  In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where the order of elements is important.

• Transformer (deep learning architecture)

  In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table.

• Attention (machine learning)

  In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence.

• Generative adversarial network

  A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence.

• Autoencoder

  An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).

• Diffusion model

  In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable generative models.

• Foundation model

  In artificial intelligence, a foundation model (FM), also known as large x model (LxM, where "x" is a variable representing any text, image, sound, etc.

• Large language model

  A large language model (LLM) is an AI model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially language generation.