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Home | Courses | Deep Learning
Course

Deep Learning

  • Teacher(s)
    Eran Raviv
  • Research field
    Data Science
  • Dates
    Period 4 - Mar 04, 2024 to Apr 26, 2024
  • Course type
    Core
  • Program year
    First
  • Credits
    4

Course description

Deep Learning course covers theoretical and practical aspects, state-of-the-art deep learning architectures, and application examples.

  1. Introduction to Deep Learning (theory and practice)
  2. Deep Learning components (gradient descent models, loss functions, avoiding over-fitting, introducing asymmetry)
  3. Feed forward neural networks
  4. Convolutional neural networks
  5. Embeddings (pre-trained embeddings, examples of pre-trained models, e.g., Word2Vec)
  6. Generative Adversarial Network (GAN)

Course literature

The following list of recommended readings (presented in alphabetical order) is considered essential for your learning experience. These articles are also part of the exam material. Changes in the reading list will be communicated on CANVAS.

Books:

  • Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep learning. MIT press.
  • Patterson, J. and Gibson, A., 2017. Deep learning: A practitioner's approach.

Selected papers, including:

  • Frank Z Xing, Erik Cambria, and Roy E Welsch. Natural language based financial forecasting: a survey. Artificial Intelligence Review, 50(1):49–73, 2018
  • Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017
  • Heaton, J.B., Polson, N.G. and Witte, J.H., 2017. Deep learning for finance: deep portfolios. Applied Stochastic Models in Business and Industry, 33(1), pp.3-12.
  • Honglak Lee, Peter Pham, Yan Largman, and Andrew Y Ng. Unsupervised feature learning for audio classification using convolutional deep belief networks. In Advances in neural information processing systems, pages 1096–1104, 2009.
  • Omer Levy and Yoav Goldberg. Neural word embedding as implicit matrix factorization. In Advances in neural information processing systems, pages 2177–2185, 2014
  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R., 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), pp.1929-1958.

Lecture notes available on CANVAS.