Draft:Outline of deep learning
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The following outline is provided as an overview of and topical guide to deep learning:
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. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Other names for deep learning
[edit]- Deep machine learning
- Deep structured learning
- Hierarchical learning
What type of thing is deep learning?
[edit]Deep learning can be described as all of the following:
Branches of deep learning
[edit]History of deep learning
[edit]Deep learning architectures
[edit]Applications of deep learning technology
[edit]- Pattern recognition –
- Classification –
- Drug discovery –
- Toxicology –
- Customer relationship management –
- Recommendation systems –
- Biomedical informatics –
Deep learning hardware
[edit]Deep learning software
[edit]- Comparison of deep learning software
- AlexNet
- Amazon SageMaker
- Apache MXNet
- Apache SINGA
- Caffe (software)
- Chainer
- Deep Learning Studio
- Deeplearning4j
- DeepSpeed
- Horovod (machine learning)
- Keras
- Microsoft Cognitive Toolkit
- MindSpore
- ML.NET
- Neural Designer
- PyTorch
- Rnn (software)
- TensorFlow
- Theano (software)
- Torch (machine learning)
- VGGNet
Deep learning libraries
[edit]Deep learning projects
[edit]Deep learning organizations
[edit]Deep learning publications
[edit]Persons influential in deep learning
[edit]See also
[edit]Further reading
[edit]- Understanding Convolutional Neural Networks (CNN), by Adit Deshpande, 2016
References
[edit]External links
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