Deep Learning

From Ioannis Kourouklides
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This page contains resources about Deep Learning and Representation Learning .

Subfields and Concepts[edit]

  • Deep Generative Models
    • Deep Directed Networks (directed graphical models)
      • Sigmoid Belief Net
      • Differentiable Generator Net
      • Variational Autoencoder (VAE)
      • Generative Adversarial Network (GAN)
      • Generative Moment Matching Network
      • Convolutional Generative Network
      • Auto-Regressive Network / Fully-visible Bayes Network (FVBN)
      • Deep Latent Gaussian Model (DLGM)
      • Deep AutoRegressive Network (DARN)
    • Deep Boltzmann Machines (undirected graphical models)
    • Deep Belief Networks (mixed graphs)
  • Deep Neural Networks (i.e. more than two hidden layers)
    • Deep Multi-Layer Perceptron (i.e. Stacked RBMs) 
    • Deep Autoencoders (i.e. two symmetrical DBN)
      • DARN 
    • Deep Neural Decision Forests 
    • Convolutional Deep Belief Network (i.e. Stacked CRBMs)  
  • Sparse Coding / Dictionary Learning
    • Sparse Autoencoders
    • Stacked Denoising Autoencoders
  • Bayesian Deep Learning
    • Bayesian Neural Networks

Online Courses[edit]

Video Lectures[edit]

Lecture Notes[edit]

Books and Book Chapters[edit]

  • Bengio, Y. (2009). Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning, 2(1), 1-127. Now Publishers.
  • Orr, G. B., & Müller, K. R. (2012). Neural Networks: Tricks of the Trade. Springer.
  • Neal, R. M. (2012). Bayesian learning for neural networks. Springer Science & Business Media.
  • Murphy, K. P. (2012). "Chapter 28: Deep Learning". Machine Learning: A Probabilistic Perspective. MIT Press.
  • Bengio, Y.,  & Courville, A. (2013). Deep Learning of Representations. Springer.
  • Deng, L., & Yu, D. (2014). Deep Learning. Foundations and Trends in Signal Processing7, 3-4.
  • Theodoridis, S. (2015). "Chapter 18: Neural Networks and Deep Learning". Machine Learning: A Bayesian and Optimization Perspective. Academic Press.
  • Gibson, A., & Patterson J. (2016). Deep Learning: A Practitioner's Approach. O'Reilly Media.
  • Bengio, Y., Goodfellow, I. J., & Courville, A. (2016). Deep Learning. MIT Press. (link)
  • Chollet, F. (2017). Deep Learning with Python. Manning Publications.
  • Gulli, A., & Kapoor, A. (2017). TensorFlow 1.x Deep Learning Cookbook. Packt Publishing.

Scholarly Articles[edit]

See Reading List and Recommended Readings for the complete list.

  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks,Volume 61, 85-117.
  • Paul, A., & Venkatasubramanian, S. (2014). Why does Deep Learning work?-A perspective from Group Theory. arXiv preprint arXiv:1412.6621.
  • Shwartz-Ziv, R., & Tishby, N. (2017). Opening the Black Box of Deep Neural Networks via Information. arXiv preprint arXiv:1703.00810.
  • Chakraborty, S., Tomsett, R., Raghavendra, R., Harborne, D., Alzantot, M., Cerutti, F., ... & Kelley, T. D. (2017). Interpretability of Deep Learning Models: A Survey of Results.



See Software Links for the complete list.

See also[edit]

Other Resources[edit]