cs501r_f2016
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| - | =CS501r, Fall 2016 - Deep Learning: Theory and Practice= | ||
| - | |||
| - | As big data and deep learning gain more prominence in both industry | ||
| - | and academia, the time seems ripe for a class focused exclusively on | ||
| - | the theory and practice of deep learning, both to understand why deep | ||
| - | learning has had such a tremendous impact across so many disciplines, | ||
| - | and also to spur research excellence in deep learning at BYU. | ||
| - | |||
| - | ==Learning activities== | ||
| - | |||
| - | This class will be a graduate-level coding class. Students will be | ||
| - | exposed to the theoretical aspects of deep learning (including | ||
| - | derivatives, regularization, and optimization theory), as well as | ||
| - | practical strategies for training large-scale networks, leveraging | ||
| - | hardware acceleration, distributing training across multiple machines, | ||
| - | and coping with massive datasets. Students will engage the material | ||
| - | primarily through weekly coding labs dedicated to implementing | ||
| - | state-of-the-art techniques, using modern deep learning software | ||
| - | frameworks. The class will culiminate with a substantial data | ||
| - | analysis project. | ||
| - | |||
| - | ==Preliminary Syllabus and topics to be covered:== | ||
| - | |||
| - | - **Basics of DNNs** | ||
| - | - 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** | ||
| - | - Tensorflow, Theano, Caffe, Keras, Torch, Mocha | ||
| - | - **Case studies / existing networks and why they're interesting** | ||
| - | - AlexNet | ||
| - | - VGG | ||
| - | - GoogLeNet / Inception | ||
| - | - ZFNet | ||
| - | - **Training & initialization** | ||
| - | - 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 | ||
| - | - Asynchronous vs. synchronous architectures | ||
| - | - **Temporal networks and how to train them** | ||
| - | - Basic RNNs and Backprop-through-time | ||
| - | - LSTMs | ||
| - | - Deep Memory Nets | ||
| - | - **Application areas** | ||
| - | - 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** | ||
| - | - tSNE embeddings | ||
| - | - deconvnets | ||
| - | - data gradients / inceptionism | ||
| - | - **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 ยท Last modified: 2021/06/30 23:42 (external edit)
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