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Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
Topological deep learning, first introduced in 2017, [147] is an emerging approach in machine learning that integrates topology with deep neural networks to address highly intricate and high-order data.
Bahasa Indonesia; עברית ... Pages in category "Deep learning" The following 48 pages are in this category, out of 48 total. This list may not reflect recent ...
The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
Generative adversarial network – Deep learning method; Generative pre-trained transformer – Type of large language model; Large language model – Type of machine learning model; Music and artificial intelligence – Usage of artificial intelligence to generate music; Generative AI pornography – Explicit material produced by generative AI
Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use backpropagation (gradient descent on a neural network) with a fixed topology.