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- | Preliminary Syllabus and topics to be covered: | ||
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- | - 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 | ||
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- | High-level implementation packages - pros and cons | ||
- | Tensorflow, Theano, Caffe, Keras, Torch, Mocha | ||
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- | Case studies / existing networks and why they're interesting | ||
- | AlexNet | ||
- | VGG | ||
- | GoogLeNet / Inception | ||
- | ZFNet | ||
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- | 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 | ||
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- | Large-scale distributed learning | ||
- | Parameter servers | ||
- | Asynchronous vs. synchronous architectures | ||
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- | Temporal networks and how to train them | ||
- | Basic RNNs | ||
- | LSTMs | ||
- | Deep Memory Nets | ||
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- | Application areas | ||
- | Deep reinforcement learning | ||
- | NN models of style vs. content (deepart.io) | ||
- | Imagenet classification | ||
- | The Neural Turing Machine | ||
- | Sentiment classification | ||
- | Word embeddings | ||
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- | Understanding and visualizing CNNs | ||
- | tSNE embeddings | ||
- | deconvnets | ||
- | data gradients / inceptionism | ||
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- | Misc | ||
- | Network compression | ||
- | Low bit-precision networks | ||
- | Sum-product networks | ||
- | Evolutionary approaches to topology discovery | ||
- | Spatial transformer networks | ||
- | Network-in-network | ||
- | Regions-with-CNN | ||