Draft:Outline of deep learning

The following outline is provided as an overview of and topical guide to deep learning:

Deep learning is a subset of machine learning methods based on neural networks with 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

• Deep machine learning
• Deep structured learning
• Hierarchical learning

What type of thing is deep learning?

Deep learning can be described as all of the following:

• Bleeding edge technology
• a branch of machine learning
  • Artificial neural networks

History of deep learning

History of deep learning

Applications of deep learning technology

• Pattern recognition –
  • Speech recognition –
  • Image recognition –
• Classification –
  • Topic classification –
  • Topic classification –
  • Sentiment analysis –
  • Question answering –
  • Language translation –
• Drug discovery –
• Toxicology –
• Customer relationship management –
• Recommendation systems –
• Biomedical informatics –

Deep learning hardware

• Graphics processing unit
• Tensor processing unit
• Vision processing unit

Deep learning software

• 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

• DistBelief
• TensorFlow

Deep learning projects

• Google Brain

Persons influential in deep learning

• Geoffrey Hinton
• Andrew Ng

Further reading

• Understanding Convolutional Neural Networks (CNN), by Adit Deshpande, 2016
  • Part 1: A Beginner's Guide To Understanding Convolutional Neural Networks
  • Part 2: A Beginner's Guide To Understanding Convolutional Neural Networks, Part 2
  • Part 3: The 9 Deep Learning Papers You Need To Know About

External links

• Deep Learning Libraries by Language
• Data Science: Data to Insights from MIT (deep learning)