cs501r_f2016
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cs501r_f2016 [2016/03/31 23:06] admin |
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| - | Preliminary Syllabus and topics to be covered: | + | ====CS501r, Fall 2016 - Deep Learning: Theory and Practice==== |
| - | - Basics of DNNs | + | [[cs501r_f2016:desc|Here is the course description.]] |
| - | - Convolution layers | + | |
| - | - Maxpooling layers | + | |
| - | - Relu units | + | |
| - | - Softmax units | + | |
| - | - Local response normalization / contrast normalization | + | |
| - | - Regularization strategies | + | |
| - | - Dropout | + | |
| - | - Dropconnect | + | |
| - | - Batch normalization | + | |
| - | - Adversarial networks | + | |
| - | - Data augmentation | + | |
| - | High-level implementation packages - pros and cons | + | [[http://cs231n.github.io/python-numpy-tutorial/|Remember, this is a great tutorial on python / numpy!]] |
| - | Tensorflow, Theano, Caffe, Keras, Torch, Mocha | + | |
| - | Case studies / existing networks and why they're interesting | + | [[cs501r_f2016:openlabtf|Some instructions for getting Tensorflow to run on the CS open labs]] |
| - | AlexNet | + | |
| - | VGG | + | |
| - | GoogLeNet / Inception | + | |
| - | ZFNet | + | |
| - | Training & initialization | + | [[https://www.dropbox.com/sh/aox63ppfd14hf7b/AABGgv56Q98ikk5I8I4bNbO3a?dl=0|All of the slides are posted on Dropbox here]] |
| - | Initialization strategies: Xavier, Gaussian, Identity, Sparse | + | |
| - | Optimization theory and algorithms | + | |
| - | Local minima; saddle points; plateaus | + | |
| - | SGD | + | |
| - | RPROP | + | |
| - | RMS prop | + | |
| - | Adagrad | + | |
| - | Adam | + | |
| - | Higher-order algorithms (LBFGS; Hessian-free; trust-region) | + | |
| - | Nesterov and momentum | + | |
| - | Large-scale distributed learning | + | ---- |
| - | Parameter servers | + | === Labs === |
| - | Asynchronous vs. synchronous architectures | + | |
| - | Temporal networks and how to train them | + | [[cs501r_f2016:lab_notes|General notes on ipython and seaborn]] |
| - | Basic RNNs | + | |
| - | LSTMs | + | |
| - | Deep Memory Nets | + | |
| - | Application areas | + | [[cs501r_f2016:lab1|Lab 1 - Anaconda and playground screenshot]] |
| - | Deep reinforcement learning | + | |
| - | NN models of style vs. content (deepart.io) | + | |
| - | Imagenet classification | + | |
| - | The Neural Turing Machine | + | |
| - | Sentiment classification | + | |
| - | Word embeddings | + | |
| - | Understanding and visualizing CNNs | + | [[cs501r_f2016:lab2|Lab 2 - Perceptron]] |
| - | tSNE embeddings | + | |
| - | deconvnets | + | [[cs501r_f2016:lab3|Lab 3 - Basic gradient descent]] |
| - | data gradients / inceptionism | + | |
| + | [[cs501r_f2016:lab4|Lab 4 - Automatic differentiation]] | ||
| + | |||
| + | [[cs501r_f2016:lab5|Lab 5 - Tensorflow image classifier]] | ||
| + | |||
| + | [[cs501r_f2016:lab5b|Lab 5b - Convolutional Tensorflow image classifier and Tensorboard]] | ||
| + | |||
| + | [[cs501r_f2016:lab6|Lab 6 - Feature zoo 1]] | ||
| + | |||
| + | [[cs501r_f2016:lab7|Lab 7 - Generative adversarial networks]] | ||
| + | |||
| + | [[cs501r_f2016:lab10|Lab 8 - RNNs, LSTMs, GRUs]] | ||
| + | |||
| + | [[cs501r_f2016:lab12|Lab 9 - Deep RL]] | ||
| + | |||
| + | [[cs501r_f2016:lab13|Lab 10 - Inceptionism / deep art]] | ||
| + | |||
| + | |||
| + | [[cs501r_f2016:fp|Final project]] | ||
| - | Misc | ||
| - | Network compression | ||
| - | Low bit-precision networks | ||
| - | Sum-product networks | ||
| - | Evolutionary approaches to topology discovery | ||
| - | Spatial transformer networks | ||
| - | Network-in-network | ||
| - | Regions-with-CNN | ||
cs501r_f2016.txt · Last modified: 2021/06/30 23:42 (external edit)
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