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‎README.md

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‎units/imagenet.md

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# Probability and Statistics
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# Peak Performance on ImageNet
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## Slides
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* [Keynote]()
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* [PDF]()
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* [Jupyter]()
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## Content
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* [Probability and Statistics](http://en.diveintodeeplearning.org/chapter_crashcourse/probability.html)
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* [Naive Bayes](http://en.diveintodeeplearning.org/chapter_crashcourse/naive-bayes.html)
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* [Sampling](http://en.diveintodeeplearning.org/chapter_crashcourse/sampling.html)
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* [Gluon CV Toolkit](https://gluon-cv.mxnet.io/)
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‎units/layers.md

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# Probability and Statistics
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# Blocks and Layers
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## Slides
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* [Keynote]()
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* [PDF]()
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* [Jupyter]()
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## Content
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* [Probability and Statistics](http://en.diveintodeeplearning.org/chapter_crashcourse/probability.html)
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* [Naive Bayes](http://en.diveintodeeplearning.org/chapter_crashcourse/naive-bayes.html)
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* [Sampling](http://en.diveintodeeplearning.org/chapter_crashcourse/sampling.html)
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* [Blocks and Layers](http://en.diveintodeeplearning.org/chapter_deep-learning-computation/model-construction.html)
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* [Parameters](http://en.diveintodeeplearning.org/chapter_deep-learning-computation/parameters.html)
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* [Deferred Initialization](http://en.diveintodeeplearning.org/chapter_deep-learning-computation/deferred-init.html)
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* [GPUs](http://en.diveintodeeplearning.org/chapter_deep-learning-computation/use-gpu.html)
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‎units/lenet.md

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# Probability and Statistics
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# Basic Convolutional Networks
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## Slides
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* [Keynote]()
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* [PDF]()
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* [Jupyter]()
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## Content
106

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* [Probability and Statistics](http://en.diveintodeeplearning.org/chapter_crashcourse/probability.html)
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* [Naive Bayes](http://en.diveintodeeplearning.org/chapter_crashcourse/naive-bayes.html)
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* [Sampling](http://en.diveintodeeplearning.org/chapter_crashcourse/sampling.html)
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* [LeNet](http://en.diveintodeeplearning.org/chapter_convolutional-neural-networks/lenet.html)
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* [AlexNet](http://en.diveintodeeplearning.org/chapter_convolutional-neural-networks/alexnet.html)
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‎units/makingitwork.md

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# Probability and Statistics
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# Making a Computer Vision Model work
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## Slides
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* [Keynote]()
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* [PDF]()
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* [Jupyter]()
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## Content
106

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* [Probability and Statistics](http://en.diveintodeeplearning.org/chapter_crashcourse/probability.html)
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* [Naive Bayes](http://en.diveintodeeplearning.org/chapter_crashcourse/naive-bayes.html)
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* [Sampling](http://en.diveintodeeplearning.org/chapter_crashcourse/sampling.html)
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Building a dog classifier from scratch. This will cover pretraining,
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tuning, etc.
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* [Image Augmentation](http://en.diveintodeeplearning.org/chapter_computer-vision/image-augmentation.html)
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* [Fine Tuning](http://en.diveintodeeplearning.org/chapter_computer-vision/fine-tuning.html)
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* [100 Dogs](http://en.diveintodeeplearning.org/chapter_computer-vision/kaggle-gluon-dog.html)
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‎units/nin.md

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# Probability and Statistics
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# Network Structures
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## Slides
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* [Keynote]()
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* [PDF]()
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* [Jupyter]()
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## Content
106

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* [Probability and Statistics](http://en.diveintodeeplearning.org/chapter_crashcourse/probability.html)
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* [Naive Bayes](http://en.diveintodeeplearning.org/chapter_crashcourse/naive-bayes.html)
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* [Sampling](http://en.diveintodeeplearning.org/chapter_crashcourse/sampling.html)
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* [Network in Network](http://en.diveintodeeplearning.org/chapter_convolutional-neural-networks/nin.html)
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* [Network with Parallel Concatenations](http://en.diveintodeeplearning.org/chapter_convolutional-neural-networks/googlenet.html)
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‎units/optimization.md

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# Probability and Statistics
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# Optimization Basics
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## Slides
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* [Keynote]()
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* [PDF]()
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* [Jupyter]()
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## Content
106

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* [Probability and Statistics](http://en.diveintodeeplearning.org/chapter_crashcourse/probability.html)
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* [Naive Bayes](http://en.diveintodeeplearning.org/chapter_crashcourse/naive-bayes.html)
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* [Sampling](http://en.diveintodeeplearning.org/chapter_crashcourse/sampling.html)
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* [Introduction to Optimization](http://en.diveintodeeplearning.org/chapter_optimization/optimization-intro.html)
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‎units/parallel.md

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# Probability and Statistics
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# Parallel Processing
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## Slides
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* [Keynote]()
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* [PDF]()
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* [Jupyter]()
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## Content
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* [Probability and Statistics](http://en.diveintodeeplearning.org/chapter_crashcourse/probability.html)
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* [Naive Bayes](http://en.diveintodeeplearning.org/chapter_crashcourse/naive-bayes.html)
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* [Sampling](http://en.diveintodeeplearning.org/chapter_crashcourse/sampling.html)
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* [Asynchrony](http://en.diveintodeeplearning.org/chapter_computational-performance/async-computation.html)
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* [Automatic Parallelization](http://en.diveintodeeplearning.org/chapter_computational-performance/auto-parallelism.html)
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* [Multiple GPUs](http://en.diveintodeeplearning.org/chapter_computational-performance/multiple-gpus.html)
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* [Multiple GPUs in Gluon](http://en.diveintodeeplearning.org/chapter_computational-performance/multiple-gpus-gluon.html)
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‎units/resnet.md

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# Probability and Statistics
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# Residual Networks and Advanced Architectures
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## Slides
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* [Keynote]()
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* [PDF]()
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* [Jupyter]()
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## Content
106

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* [Probability and Statistics](http://en.diveintodeeplearning.org/chapter_crashcourse/probability.html)
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* [Naive Bayes](http://en.diveintodeeplearning.org/chapter_crashcourse/naive-bayes.html)
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* [Sampling](http://en.diveintodeeplearning.org/chapter_crashcourse/sampling.html)
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* [Residual Networks](http://en.diveintodeeplearning.org/chapter_convolutional-neural-networks/resnet.html)
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* [Densely Connected Networks](http://en.diveintodeeplearning.org/chapter_convolutional-neural-networks/densenet.html)
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‎units/rnn.md

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# Probability and Statistics
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# Recurrent Neural Networks
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## Slides
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* [Keynote]()
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* [PDF]()
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* [Jupyter]()
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## Content
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* [Probability and Statistics](http://en.diveintodeeplearning.org/chapter_crashcourse/probability.html)
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* [Naive Bayes](http://en.diveintodeeplearning.org/chapter_crashcourse/naive-bayes.html)
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* [Sampling](http://en.diveintodeeplearning.org/chapter_crashcourse/sampling.html)
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* [Building an RNN](http://en.diveintodeeplearning.org/chapter_recurrent-neural-networks/rnn-scratch.html)
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* [Building an RNN in Gluon](http://en.diveintodeeplearning.org/chapter_recurrent-neural-networks/rnn-gluon.html)
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* [Backpropagation through Time](http://en.diveintodeeplearning.org/chapter_recurrent-neural-networks/bptt.html)
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‎units/sequence.md

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# Probability and Statistics
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# Sequence Models and Language
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## Slides
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* [Keynote]()
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* [PDF]()
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* [Jupyter]()
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## Content
106

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* [Probability and Statistics](http://en.diveintodeeplearning.org/chapter_crashcourse/probability.html)
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* [Naive Bayes](http://en.diveintodeeplearning.org/chapter_crashcourse/naive-bayes.html)
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* [Sampling](http://en.diveintodeeplearning.org/chapter_crashcourse/sampling.html)
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* [Sequence Models](http://en.diveintodeeplearning.org/chapter_recurrent-neural-networks/sequence.html)
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* [Language Models](http://en.diveintodeeplearning.org/chapter_recurrent-neural-networks/lang-model.html)
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* [Recurrent Neural Networks](http://en.diveintodeeplearning.org/chapter_recurrent-neural-networks/rnn.html)
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* [Text Preprocessing](http://en.diveintodeeplearning.org/chapter_recurrent-neural-networks/lang-model-dataset.html)
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‎units/sgd.md

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# Probability and Statistics
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# Stochastic Gradient Descent for Deep Learning
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## Slides
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* [Keynote]()
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* [PDF]()
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* [Jupyter]()
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## Content
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* [Probability and Statistics](http://en.diveintodeeplearning.org/chapter_crashcourse/probability.html)
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* [Naive Bayes](http://en.diveintodeeplearning.org/chapter_crashcourse/naive-bayes.html)
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* [Sampling](http://en.diveintodeeplearning.org/chapter_crashcourse/sampling.html)
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* [Stochastic Gradient Descent](http://en.diveintodeeplearning.org/chapter_optimization/gd-sgd.html)
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* [Batching](http://en.diveintodeeplearning.org/chapter_optimization/minibatch-sgd.html)
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* [Momentum](http://en.diveintodeeplearning.org/chapter_optimization/momentum.html)
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