Difference between revisions of "Class meeting for 10-605 Deep Learning"
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− | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall | + | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2017]]. |
=== 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]. |
+ | * Lecture 2: [http://www.cs.cmu.edu/~wcohen/10-605/deep-2.pptx Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/deep-2.pdf PDF]. | ||
+ | |||
+ | * Lecture 3: [http://www.cs.cmu.edu/~wcohen/10-605/deep-3.pptx Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/deep-3.pdf PDF]. | ||
+ | |||
+ | === Quizzes === | ||
+ | |||
+ | * [https://qna.cs.cmu.edu/#/pages/view/75 Quiz for lecture 1] | ||
+ | * [https://qna.cs.cmu.edu/#/pages/view/79 Quiz for lecture 2] | ||
+ | * [https://qna.cs.cmu.edu/#/pages/view/212 Quiz for lecture 3] | ||
+ | |||
+ | === Sample code === | ||
+ | |||
+ | * [http://www.cs.cmu.edu/~wcohen/10-605/code/xman.py Expression manager] | ||
+ | * [http://www.cs.cmu.edu/~wcohen/10-605/code/sample-use-of-xman.py Sample use of the expression manager] | ||
=== Readings === | === Readings === | ||
Line 15: | Line 29: | ||
* More general neural networks: | * More general neural networks: | ||
** [http://neuralnetworksanddeeplearning.com/index.html 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. | ** [http://neuralnetworksanddeeplearning.com/index.html 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 [http://www.deeplearningbook.org/ the MIT Press book (in preparation) from Bengio] - it's very complete but also fairly technical. | + | ** For much much more detail, look at [http://www.deeplearningbook.org/ the MIT Press book (in preparation) from Bengio] - it's very complete but also fairly technical. |
=== Things to remember === | === Things to remember === | ||
Line 24: | Line 38: | ||
* Matrix operations and GPU training | * Matrix operations and GPU training | ||
* ReLU, cross-entropy, softmax | * ReLU, cross-entropy, softmax | ||
− | * How backprop can be generalized to a sequence of assignment operations | + | * How backprop can be generalized to a sequence of assignment operations (autodiff) |
+ | ** Wengert lists | ||
+ | ** How to evaluate and differentiate a Wengert list | ||
+ | * Common architectures | ||
+ | ** Multi-layer perceptron | ||
+ | ** Recursive NNs (RNNS) and Long/short term memory networks (LSTMs) | ||
+ | ** Convolutional Networks (CNNs) |
Latest revision as of 13:38, 31 October 2017
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2017.
Slides
- Lecture 1: Powerpoint, PDF.
- Lecture 2: Powerpoint, PDF.
- Lecture 3: Powerpoint, PDF.
Quizzes
Sample code
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 (autodiff)
- Wengert lists
- How to evaluate and differentiate a Wengert list
- Common architectures
- Multi-layer perceptron
- Recursive NNs (RNNS) and Long/short term memory networks (LSTMs)
- Convolutional Networks (CNNs)