Difference between revisions of "Class meeting for 10-605 Deep Learning"
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=== Slides === | === Slides === | ||
| − | * | + | * Lecture 1: [http://www.cs.cmu.edu/~wcohen/10-605/deep-1.pptx Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/deep-1.pdf PDF]. |
=== Readings === | === Readings === | ||
Revision as of 14:02, 24 October 2016
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2016.
Slides
- Lecture 1: Powerpoint, PDF.
Readings
- Automatic differentiation:
- William's notes on automatic differentiation, and the Python code for a simple Wengart list generator and a sample use of a one.
- Domke's blog post - clear but not much detail - and another nice blog post.
- The clearest paper I've found is Reverse-Mode AD in a Functional Framework: Lambda the Ultimate Backpropagator
- More general neural networks:
- 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 much much 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
- How backprop can be generalized to a sequence of assignment operations
