Practical Machine Learning - Deep Learning
Lecturer: Jian Li
| Oct 7 | Basics - Logistic regression, SVM, Gradient Descent, Stochastic GD, Neural Networks, Back Propagation, Training Tricks | slides |
| Oct 21 | CNN, PCA, Several CNN architectures, Visualize CNN (deconv net), Deep Dream, Neural Art | slides |
| Oct 28 | RNN, Bidirectional RNN, Grad vanishing & Explosion, LSTM, Image Captioning | slides |
| Nov 3 | Autoencoder, Attention (spatial transformer), Multi-modal learning, Neural Turing Machine, Memory Networks, Generative Adversarial Net | slides |
Links:
CS224d: Deep Learning for Natural Language Processing
http://cs224d.stanford.edu/reports_2016.html
CS231n: Convolutional Neural Networks for Visual Recognition
http://cs231n.stanford.edu/project.html
Nando de Freitas’s class at Oxford
https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
Jeff Hinton's Deep Learning Course
Deepmind
https://deepmind.com/research/publications/
Deep Learning Book (Ian Goodfellow and Yoshua Bengio and Aaron Courville)
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