Difference between revisions of "10-601 Deep Learning 1"
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* [http://www.cs.cmu.edu/~wcohen/10-601/deep-1.pptx Slides in PowerPoint],[http://www.cs.cmu.edu/~wcohen/10-601/deep-1.pdf Slides in PDF]. | * [http://www.cs.cmu.edu/~wcohen/10-601/deep-1.pptx Slides in PowerPoint],[http://www.cs.cmu.edu/~wcohen/10-601/deep-1.pdf Slides in PDF]. | ||
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=== Readings === | === Readings === | ||
Latest revision as of 15:18, 11 April 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
This area is moving very fast and the textbooks are not up-to-date. Some recommended readings:
- Neural Networks and Deep Learning An online book by Michael Nielsen, pitched at an appropriate level for 10-601, which has a bunch of exercises and on-line sample programs in Python.
For more detail, look at the MIT Press book (in preparation) from Bengio - it's very complete but also fairly technical.
Things to remember
- The underlying reasons deep networks are hard to train
- Exploding/vanishing gradients
- Saturation
- The importance of key recent advances in neural networks:
- Matrix operations and GPU training
- ReLU, cross-entropy, softmax
