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instillai / repository
:satellite: All You Need to Know About Deep Learning - A kick-starter
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
################################################### Deep Learning - All You Need to Know ###################################################
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################## Table of Contents ################## .. contents:: :local: :depth: 4
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Introduction
The purpose of this project is to introduce a shortcut to developers and researcher for finding useful resources about Deep Learning.
There are different motivations for this open source project.
.. -------------------- .. Why Deep Learning? .. --------------------
There are other repositories similar to this repository that are very comprehensive and useful and to be honest they made me ponder if there is a necessity for this repository!
The point of this repository is that the resources are being targeted. The organization of the resources is such that the user can easily find the things he/she is looking for. We divided the resources to a large number of categories that in the beginning one may have a headache!!! However, if someone knows what is being located, it is very easy to find the most related resources. Even if someone doesn't know what to look for, in the beginning, the general resources have been provided.
.. ================================================ .. How to make the most of this effort .. ================================================
Papers
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This chapter is associated with the papers published in deep learning.
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Imagenet classification with deep convolutional neural networks :
[Paper <http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks>][Code <https://github.com/dontfollowmeimcrazy/imagenet>]
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Convolutional Neural Networks for Sentence Classification :
[Paper <https://arxiv.org/abs/1408.5882>][Code <https://github.com/yoonkim/CNN_sentence>]
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Large-scale Video Classification with Convolutional Neural Networks :
[Paper <https://www.cv-foundation.org/openaccess/content_cvpr_2014/html/Karpathy_Large-scale_Video_Classification_2014_CVPR_paper.html>][Project Page <https://cs.stanford.edu/people/karpathy/deepvideo/>]
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Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks :
[Paper <https://www.cv-foundation.org/openaccess/content_cvpr_2014/html/Oquab_Learning_and_Transferring_2014_CVPR_paper.html>_]
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Deep convolutional neural networks for LVCSR :
[Paper <https://ieeexplore.ieee.org/abstract/document/6639347/&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=KknXWYbGFMbFjwSsyICADQ&scisig=AAGBfm2F0Zlu0ciUwadzshNNm80IQQhuhA>_]
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Face recognition: a convolutional neural-network approach :
[Paper <https://ieeexplore.ieee.org/abstract/document/554195/>_]
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An empirical exploration of recurrent network architectures :
[Paper <http://proceedings.mlr.press/v37/jozefowicz15.pdf?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue>][Code <https://github.com/debajyotidatta/RecurrentArchitectures>]
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LSTM: A search space odyssey :
[Paper <https://ieeexplore.ieee.org/abstract/document/7508408/>][Code <https://github.com/fomorians/lstm-odyssey>]
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On the difficulty of training recurrent neural networks :
[Paper <http://proceedings.mlr.press/v28/pascanu13.pdf>][Code <https://github.com/pascanur/trainingRNNs>]
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Learning to forget: Continual prediction with LSTM :
[Paper <http://digital-library.theiet.org/content/conferences/10.1049/cp_19991218>_]
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Extracting and composing robust features with denoising autoencoders :
[Paper <https://dl.acm.org/citation.cfm?id=1390294>_]
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Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion :
[Paper <http://www.jmlr.org/papers/v11/vincent10a.html>][Code <https://github.com/rajarsheem/libsdae-autoencoder-tensorflow>]
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Adversarial Autoencoders :
[Paper <https://arxiv.org/abs/1511.05644>][Code <https://github.com/conan7882/adversarial-autoencoders>]
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Autoencoders, Unsupervised Learning, and Deep Architectures :
[Paper <http://proceedings.mlr.press/v27/baldi12a/baldi12a.pdf>_]
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Reducing the Dimensionality of Data with Neural Networks :
[Paper <http://science.sciencemag.org/content/313/5786/504>][Code <https://github.com/jordn/autoencoder>]
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Exploiting generative models discriminative classifiers :
[Paper <http://papers.nips.cc/paper/1520-exploiting-generative-models-in-discriminative-classifiers.pdf>_]
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Semi-supervised Learning with Deep Generative Models :
[Paper <http://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models>][Code <https://github.com/wohlert/semi-supervised-pytorch>]
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Generative Adversarial Nets :
[Paper <http://papers.nips.cc/paper/5423-generative-adversarial-nets>][Code <https://github.com/goodfeli/adversarial>]
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Generalized Denoising Auto-Encoders as Generative Models :
[Paper <http://papers.nips.cc/paper/5023-generalized-denoising-auto-encoders-as-generative-models>_]
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks :
[Paper <https://arxiv.org/abs/1511.06434>][Code <https://github.com/carpedm20/DCGAN-tensorflow>]
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models :
[Paper <https://arxiv.org/abs/1401.4082>_]
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Probabilistic models of cognition: exploring representations and inductive biases :
[Paper <https://www.sciencedirect.com/science/article/pii/S1364661310001129>_]
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On deep generative models with applications to recognition :
[Paper <https://ieeexplore.ieee.org/abstract/document/5995710/>_]
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift :
[Paper <https://arxiv.org/abs/1502.03167>_]
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting :
[Paper <http://www.jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf?utm_content=buffer79b43&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer>_]
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Training Very Deep Networks :
[Paper <http://papers.nips.cc/paper/5850-training-very-deep-networks>_]
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification :
[Paper <https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf>_]
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Large Scale Distributed Deep Networks :
[Paper <http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks>_]
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks :
[Paper <https://arxiv.org/abs/1511.06434>][Code <https://github.com/Newmu/dcgan_code>]
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Representation Learning: A Review and New Perspectives :
[Paper <https://ieeexplore.ieee.org/abstract/document/6472238/>_]
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets :
[Paper <http://papers.nips.cc/paper/6399-infogan-interpretable-representation>_][`Code <https://github.com/openai/InfoGA