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Artificial neural networks
Backpropagation
Convolutional neural networks
Deep learning
Deep reinforcement learning
Diffusion generative models
Large language models
Machine learning
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Artificial neural networks
→
Deep learning
applied in
Deep learning learns representations using multilayer neural architectures; this does not imply that all neural networks fall within deep learning.
Backpropagation
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Deep learning
applied in
Modern deep networks often use backpropagation to compute gradients; this relationship does not present it as the sole necessary condition for every method of training deep networks.
Convolutional neural networks
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Deep learning
applied in
Deep convolutional networks are a specific architectural approach through which deep learning is applied to document and visual recognition.
Deep learning
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Deep reinforcement learning
applied in
Methods such as DQN apply deep representations to action-value estimation, combining perceptual learning with reward-driven decision-making.
Deep learning
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Diffusion generative models
applied in
Deep networks can be used to learn reverse diffusion or denoising processes; the 2015 and 2020 papers provide corresponding examples of generative modeling.
Deep learning
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Large language models
applied in
The large-scale pretrained language models covered here use deep networks to learn sequence distributions; deep learning provides the training and representation methods.
Deep learning
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Machine learning
specializes
Deep learning uses multiple layers of trainable representations to learn, making it a class of machine-learning methods.