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 multilayered 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.

• Deep machine learning
• Deep structured learning
• Hierarchical learning

Deep learning can be described as all of the following:

• A type of technology
  • Bleeding edge technology
  • Computer technology
    • Artificial neural networks
• A branch of science
  • A branch of applied science
    • A branch of computer science
      • A branch of artificial intelligence
        • a branch of machine learning

History of deep learning

• 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 –

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

• 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

• DistBelief
• TensorFlow

• Google Brain

• Geoffrey Hinton
• Andrew Ng

• 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

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