https://github.com/rusty1s/deep-learning-cheatsheet
https://github.com/rusty1s/deep-learning-cheatsheet
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/rusty1s/deep-learning-cheatsheet
- Owner: rusty1s
- Created: 2017-08-02T07:48:13.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2018-02-21T05:45:01.000Z (over 8 years ago)
- Last Synced: 2025-03-29T21:22:26.106Z (over 1 year ago)
- Language: TeX
- Size: 45.9 KB
- Stars: 99
- Watchers: 5
- Forks: 29
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Deep Learning Cheatsheet
General | [Graphs](graph.md)
## Introduction
* [Machine Learning Glossary](https://developers.google.com/machine-learning/glossary/)
* [Essential Machine Learning Cheatsheets](https://github.com/kailashahirwar/cheatsheets-ai)
* [Neural Networks and Deep Learning](http://www.neuralnetworksanddeeplearning.com) [Free Online Book]
* [Free Deep Learning Book](http://www.datasciencecentral.com/profiles/blogs/free-deep-learning-book-mit-press) [MIT Press]
* [Andrew Ng's machine learning course at Coursera](https://www.coursera.org/learn/machine-learning) [[Material](http://cs229.stanford.edu/materials.html)]
* [Deep Learning by Google](https://www.udacity.com/course/deep-learning--ud730#)
* [Deep Learning in Neural Networks: An Overview](https://arxiv.org/pdf/1404.7828.pdf)
* [How To Become A Machine Learning Engineer: Learning Path](https://hackernoon.com/learning-path-for-machine-learning-engineer-a7d5dc9de4a4)
* [Awesome Deep Vision](https://www.github.com/kjw0612/awesome-deep-vision)
* [A Guide to Deep Learning](http://yerevann.com/a-guide-to-deep-learning/?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue)
* [Deep Learning Weekly](http://www.deeplearningweekly.com) [Weekly Newsletter Subscription]
* [A Year of Artificial Intelligence](https://ayearofai.com/) [Blog]
* [Introduction to Convolutional Neural Networks](http://cs.nju.edu.cn/wujx/paper/CNN.pdf)
* [Applied Deep Learning](https://medium.com/towards-data-science/applied-deep-learning-part-1-artificial-neural-networks-d7834f67a4f6)
* [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471) [Medium Series]
* [Machine Learning for Humans](https://medium.com/machine-learning-for-humans) [Medium Series]
* [My Neural network ins't working! What should I do?](http://theorangeduck.com/page/neural-network-not-working)
* [The math of neural networks](http://himarsh.org/the-math-neural-networks/)
* [Everything you need to know about Neural Networks](https://hackernoon.com/everything-you-need-to-know-about-neural-networks-8988c3ee4491)
## Python
* [Learn Python the hard way](https://learnpythonthehardway.org/book/)
* [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)
* [Styleguide](https://www.python.org/dev/peps/pep-0008/) [PEP8]
* [TensorFlow](https://www.tensorflow.org) [Open Source Library]
* [PyCUDA](https://mathema.tician.de/software/pycuda/)
* [Cython](http://cython.org/)
* [Pure Python Mode](http://cython.readthedocs.io/en/latest/src/tutorial/pure.html)
### TensorFlow
* [Official](https://www.tensorflow.org/versions/r0.11/tutorials/index.html)
* [Adding a New Op](https://www.tensorflow.org/extend/adding_an_op) [Official]
* [TensorFlow in a Nutshell](http://camron.xyz)
* [Source Code Examples](https://github.com/aymericdamien/TensorFlow-Examples)
* [Effictive TensorFlow](https://github.com/vahidk/EffectiveTensorflow) [TensorFlow tutorials and best practices]
### PyTorch
* [Official](http://pytorch.org/)
* [PyTorch Examples](https://github.com/jcjohnson/pytorch-examples)
# Conferences
* [ICLR 2018](https://chillee.github.io/OpenReviewExplorer/)
## Architectures
* [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556v6.pdf) [VGG]
* [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5MB model size](https://arxiv.org/pdf/1602.07360.pdf)
* [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://arxiv.org/pdf/1406.4729.pdf)
* [Dynamically Expandable Neural Networks](https://buzzrobot.com/dynamically-expandable-neural-networks-ce75ff2b69cf)
### AutoEncoder and GANs
* [Generative Adversarial Networks (GANs): Engine and Applications](https://blog.statsbot.co/generative-adversarial-networks-gans-engine-and-applications-f96291965b47)
* [GANs are Broken in More than One Way: The Numerics of GANs](http://www.inference.vc/my-notes-on-the-numerics-of-gans/)
* [Towards data set augmentation with GANs](https://medium.com/towards-data-science/towards-data-set-augmentation-with-gans-9dd64e9628e6)
* [How does the unpooling and deconvolution work in DeConvNet](https://stackoverflow.com/questions/35049197/how-does-the-unpooling-and-deconvolution-work-in-deconvnet)
* [What are deconvoltional layers?](https://datascience.stackexchange.com/questions/6107/what-are-deconvolutional-layers)
* [Visualizing and Understanding Convolutional Networks](https://arxiv.org/pdf/1311.2901v3.pdf) [Original Unpooling Paper]
## Optimizer
* [A method for stochastic optimization](https://arxiv.org/pdf/1412.6980v8.pdf) [Adam Optimizer]
## Overfitting
* [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf)
* [A Simple Weight Decay Can Improve Generalization](https://papers.nips.cc/paper/563-a-simple-weight-decay-can-improve-generalization.pdf)
* [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/pdf/1502.03167.pdf)
* [Batch Normalization - What the hey?](https://gab41.lab41.org/batch-normalization-what-the-hey-d480039a9e3b) [Blog]
## Recurrent Neural Networks
* [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
## Datasets
* [Overview](http://deeplearning.net/datasets/)
### Images
* [MNIST](http://yann.lecun.com/exdb/mnist/)
* [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist)
* [CIFAR-10/100](http://www.cs.toronto.edu/%7Ekriz/cifar.html)
* [STL-10](https://cs.stanford.edu/~acoates/stl10/)
* [SVHN](http://ufldl.stanford.edu/housenumbers/)
* [ImageNet](http://image-net.org/)
* [ILSVRC2014](http://image-net.org/challenges/LSVRC/2014/download-images-5jj5.php)
* [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/)
* [Animals with Attributes](https://cvml.ist.ac.at/AwA2/)
### Meshes
* [MPI FAUST Dataset](http://faust.is.tue.mpg.de/)
* [Tosca](http://tosca.cs.technion.ac.il/book/resources_data.html)
## Classification
* [Image Classification datasets results](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html)