{"id":15639067,"url":"https://github.com/rusty1s/deep-learning-cheatsheet","last_synced_at":"2025-08-24T13:08:01.967Z","repository":{"id":94688487,"uuid":"99088165","full_name":"rusty1s/deep-learning-cheatsheet","owner":"rusty1s","description":null,"archived":false,"fork":false,"pushed_at":"2018-02-21T05:45:01.000Z","size":47,"stargazers_count":99,"open_issues_count":0,"forks_count":29,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-03-29T21:22:26.106Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rusty1s.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-08-02T07:48:13.000Z","updated_at":"2025-02-24T09:40:27.000Z","dependencies_parsed_at":"2023-03-13T16:57:59.219Z","dependency_job_id":null,"html_url":"https://github.com/rusty1s/deep-learning-cheatsheet","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rusty1s%2Fdeep-learning-cheatsheet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rusty1s%2Fdeep-learning-cheatsheet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rusty1s%2Fdeep-learning-cheatsheet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rusty1s%2Fdeep-learning-cheatsheet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rusty1s","download_url":"https://codeload.github.com/rusty1s/deep-learning-cheatsheet/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250360249,"owners_count":21417718,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-03T11:24:32.990Z","updated_at":"2025-04-23T02:52:18.686Z","avatar_url":"https://github.com/rusty1s.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deep Learning Cheatsheet\n\nGeneral | [Graphs](graph.md)\n\n## Introduction\n\n* [Machine Learning Glossary](https://developers.google.com/machine-learning/glossary/)\n* [Essential Machine Learning Cheatsheets](https://github.com/kailashahirwar/cheatsheets-ai)\n* [Neural Networks and Deep Learning](http://www.neuralnetworksanddeeplearning.com) [Free Online Book]\n* [Free Deep Learning Book](http://www.datasciencecentral.com/profiles/blogs/free-deep-learning-book-mit-press) [MIT Press]\n* [Andrew Ng's machine learning course at Coursera](https://www.coursera.org/learn/machine-learning) [[Material](http://cs229.stanford.edu/materials.html)]\n* [Deep Learning by Google](https://www.udacity.com/course/deep-learning--ud730#)\n* [Deep Learning in Neural Networks: An Overview](https://arxiv.org/pdf/1404.7828.pdf)\n* [How To Become A Machine Learning Engineer: Learning Path](https://hackernoon.com/learning-path-for-machine-learning-engineer-a7d5dc9de4a4)\n* [Awesome Deep Vision](https://www.github.com/kjw0612/awesome-deep-vision)\n* [A Guide to Deep Learning](http://yerevann.com/a-guide-to-deep-learning/?utm_campaign=Revue%20newsletter\u0026utm_medium=Newsletter\u0026utm_source=revue)\n* [Deep Learning Weekly](http://www.deeplearningweekly.com) [Weekly Newsletter Subscription]\n* [A Year of Artificial Intelligence](https://ayearofai.com/) [Blog]\n* [Introduction to Convolutional Neural Networks](http://cs.nju.edu.cn/wujx/paper/CNN.pdf)\n* [Applied Deep Learning](https://medium.com/towards-data-science/applied-deep-learning-part-1-artificial-neural-networks-d7834f67a4f6)\n* [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471) [Medium Series]\n* [Machine Learning for Humans](https://medium.com/machine-learning-for-humans) [Medium Series]\n* [My Neural network ins't working! What should I do?](http://theorangeduck.com/page/neural-network-not-working)\n* [The math of neural networks](http://himarsh.org/the-math-neural-networks/)\n* [Everything you need to know about Neural Networks](https://hackernoon.com/everything-you-need-to-know-about-neural-networks-8988c3ee4491)\n\n## Python\n\n* [Learn Python the hard way](https://learnpythonthehardway.org/book/)\n* [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)\n* [Styleguide](https://www.python.org/dev/peps/pep-0008/) [PEP8]\n* [TensorFlow](https://www.tensorflow.org) [Open Source Library]\n* [PyCUDA](https://mathema.tician.de/software/pycuda/)\n* [Cython](http://cython.org/)\n  * [Pure Python Mode](http://cython.readthedocs.io/en/latest/src/tutorial/pure.html)\n\n### TensorFlow\n\n* [Official](https://www.tensorflow.org/versions/r0.11/tutorials/index.html)\n* [Adding a New Op](https://www.tensorflow.org/extend/adding_an_op) [Official]\n* [TensorFlow in a Nutshell](http://camron.xyz)\n* [Source Code Examples](https://github.com/aymericdamien/TensorFlow-Examples)\n* [Effictive TensorFlow](https://github.com/vahidk/EffectiveTensorflow) [TensorFlow tutorials and best practices]\n\n### PyTorch\n\n* [Official](http://pytorch.org/)\n* [PyTorch Examples](https://github.com/jcjohnson/pytorch-examples)\n\n# Conferences\n\n* [ICLR 2018](https://chillee.github.io/OpenReviewExplorer/)\n\n## Architectures\n\n* [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556v6.pdf) [VGG]\n* [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5MB model size](https://arxiv.org/pdf/1602.07360.pdf)\n* [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://arxiv.org/pdf/1406.4729.pdf)\n* [Dynamically Expandable Neural Networks](https://buzzrobot.com/dynamically-expandable-neural-networks-ce75ff2b69cf)\n\n### AutoEncoder and GANs\n\n* [Generative Adversarial Networks (GANs): Engine and Applications](https://blog.statsbot.co/generative-adversarial-networks-gans-engine-and-applications-f96291965b47)\n* [GANs are Broken in More than One Way: The Numerics of GANs](http://www.inference.vc/my-notes-on-the-numerics-of-gans/)\n* [Towards data set augmentation with GANs](https://medium.com/towards-data-science/towards-data-set-augmentation-with-gans-9dd64e9628e6)\n* [How does the unpooling and deconvolution work in DeConvNet](https://stackoverflow.com/questions/35049197/how-does-the-unpooling-and-deconvolution-work-in-deconvnet)\n* [What are deconvoltional layers?](https://datascience.stackexchange.com/questions/6107/what-are-deconvolutional-layers)\n* [Visualizing and Understanding Convolutional Networks](https://arxiv.org/pdf/1311.2901v3.pdf) [Original Unpooling Paper]\n\n## Optimizer\n\n* [A method for stochastic optimization](https://arxiv.org/pdf/1412.6980v8.pdf) [Adam Optimizer]\n\n## Overfitting\n\n* [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf)\n* [A Simple Weight Decay Can Improve Generalization](https://papers.nips.cc/paper/563-a-simple-weight-decay-can-improve-generalization.pdf)\n* [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/pdf/1502.03167.pdf)\n  * [Batch Normalization - What the hey?](https://gab41.lab41.org/batch-normalization-what-the-hey-d480039a9e3b) [Blog]\n\n## Recurrent Neural Networks\n\n* [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)\n\n## Datasets\n\n* [Overview](http://deeplearning.net/datasets/)\n\n### Images\n\n* [MNIST](http://yann.lecun.com/exdb/mnist/)\n* [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist)\n* [CIFAR-10/100](http://www.cs.toronto.edu/%7Ekriz/cifar.html)\n* [STL-10](https://cs.stanford.edu/~acoates/stl10/)\n* [SVHN](http://ufldl.stanford.edu/housenumbers/)\n* [ImageNet](http://image-net.org/)\n  * [ILSVRC2014](http://image-net.org/challenges/LSVRC/2014/download-images-5jj5.php)\n* [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/)\n* [Animals with Attributes](https://cvml.ist.ac.at/AwA2/)\n\n### Meshes\n\n* [MPI FAUST Dataset](http://faust.is.tue.mpg.de/)\n* [Tosca](http://tosca.cs.technion.ac.il/book/resources_data.html)\n\n## Classification\n\n* [Image Classification datasets results](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frusty1s%2Fdeep-learning-cheatsheet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frusty1s%2Fdeep-learning-cheatsheet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frusty1s%2Fdeep-learning-cheatsheet/lists"}