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- DictionaryDeep learning
noun
- 1. a type of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher level features from data: "the prototype will use a combination of deep learning, natural language processing, and dynamic network analysis to detect and examine the cross-platform spread of disinformation"
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Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.
Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Dec 12, 2023 · Deep learning is just a type of machine learning, inspired by the structure of the human brain. Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses multi-layered structures of algorithms called neural networks.
May 26, 2024 · What is Deep Learning? The definition of Deep learning is that it is the branch of machine learning that is based on artificial neural network architecture.
Mar 26, 2024 · Deep learning is a method that trains computers to process information in a way that mimics human neural processes. Learn more about deep learning examples and applications in this article.
Deep learning is a type of machine learning that uses artificial neural networks to learn from data. Artificial neural networks are inspired by the human brain, and they...
Apr 29, 2024 · Deep learning, also known as neural organized learning, occurs when artificial neural networks learn from large volumes of data. Deep learning algorithms perform tasks repeatedly, tweaking them each time to improve the outcome. The algorithms depend on vast amounts of data to drive "learning."
