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https://github.com/hggeorgiev/awesome-deep-learning-materials

Curated materials from Centroida's Deep Learning team
https://github.com/hggeorgiev/awesome-deep-learning-materials

List: awesome-deep-learning-materials

artificial-intelligence deep-learning deep-neural-networks machine-learning

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Curated materials from Centroida's Deep Learning team

Awesome Lists containing this project

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# awesome-deep-learning-materials [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)



Curated learning materials from [Centroida](https://github.com/Centroida)'s Deep Learning team



Contributions are welcome



## Computer Vision & Convolutional Neural Networks



### Courses

* [Andrej Karpathy's YouTube channel - CS 231n Convolutional Neural Networks for Visual Recognition](https://www.youtube.com/channel/UCPk8m_r6fkUSYmvgCBwq-sw/videos)



### Articles & Pieces

* [Object Detection using Deep Learning for advanced users (Part-1)](https://medium.com/ilenze-com/object-detection-using-deep-learning-for-advanced-users-part-1-183bbbb08b19)

* [Creating a Modern OCR Pipeline Using Computer Vision and Deep Learning](https://blogs.dropbox.com/tech/2017/04/creating-a-modern-ocr-pipeline-using-computer-vision-and-deep-learning/)

* [Building powerful image classification models using very little data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)

* [How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native](https://medium.com/@timanglade/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3)

* [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.github.io/convolutional-networks/)



### Research Papers

* [One weird trick for parallelizing convolutional neural networks](https://arxiv.org/pdf/1404.5997v2.pdf)

* [Colorful Image Colorization](https://arxiv.org/pdf/1603.08511.pdf)

* [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/pdf/1602.07261.pdf)

* [Speed/accuracy trade-offs for modern convolutional object detectors](https://arxiv.org/pdf/1611.10012.pdf)

* [Very Deep Convolutional Networks for large-scale image recognition](https://arxiv.org/pdf/1409.1556.pdf)

* [Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/pdf/1512.00567.pdf)

* [Show, Attend and Tell: Neural Image Caption](https://arxiv.org/pdf/1502.03044v3.pdf)

* [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://arxiv.org/pdf/1406.4729.pdf)

* [SSD: Single Shot MultiBox Detector](https://arxiv.org/pdf/1512.02325.pdf)

* [Scene Labeling with LSTM Recurrent Neural Networks](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Byeon_Scene_Labeling_With_2015_CVPR_paper.pdf)

* [Generation with Visual Attention](https://arxiv.org/pdf/1502.03044v3.pdf)

* [Attentive Recurrent Comparators](https://arxiv.org/pdf/1703.00767.pdf)



## Natural Language Processing & Recurrent Neural Networks



### Courses

* [[TORONTO U] Supervised Sequence Labelling with Recurrent

Neural Networks](https://www.cs.toronto.edu/~graves/preprint.pdf)



### Articles & Pieces

* [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)

* [Attention and Memory in Deep Learning and NLP](http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/)

* [On word embeddings - Part 1](http://ruder.io/word-embeddings-1/)

* [LSTM with word2vec embeddings](https://www.kaggle.com/lystdo/lstm-with-word2vec-embeddings)

* [Latent Dirichlet Allocation](http://videolectures.net/mlss09uk_blei_tm/)

* [Structured Self-Attentive Sentence Embedding](https://arxiv.org/pdf/1703.03130.pdf)

* [Extracting Information from Text - Chapter 7](http://www.nltk.org/book/ch07.html)



### Research Papers

* [NLP from scratch](https://arxiv.org/pdf/1103.0398.pdf)

* [Learning to Generate Reviews and Discovering Sentiment](https://arxiv.org/pdf/1704.01444.pdf)

* [An Empirical Exploration of Recurrent Network Architectures](http://proceedings.mlr.press/v37/jozefowicz15.pdf)

* [word2vec Parameter Learning Explained](https://arxiv.org/pdf/1411.2738v4.pdf)

* [Automatic Log Analysis using

Machine Learning](http://uu.diva-portal.org/smash/get/diva2:667650/FULLTEXT01.pdf)

* [Skip-Thought Vectors](https://arxiv.org/pdf/1506.06726.pdf)

* [Reasoning about Entailment with Neural Attention](https://arxiv.org/pdf/1509.06664.pdf)



## Examples

* [Practical seq2seq](https://github.com/farizrahman4u/seq2seq)

* [Attention Entailment with Keras](https://github.com/shyamupa/snli-entailment/blob/master/amodel.py)



## Security



### Articles & Pieces

* [Delving deep into Generative Adversarial Networks (GANs)](https://github.com/GKalliatakis/Delving-deep-into-GANs)

* [Attacking Machine Learning with Adversarial Examples](https://blog.openai.com/adversarial-example-research/)

* [Adversarial Examples for Semantic Segmentation and Object Detection](https://arxiv.org/pdf/1703.08603.pdf)

* [Devling into Transferrable Adversarial Examples

And Black-box Attacks](https://arxiv.org/pdf/1611.02770.pdf)



### Research Papers

* [ZOO: Zeroth Order Optimization Based Black-box Attacks to

Deep Neural Networks without Training Substitute Models](https://arxiv.org/pdf/1708.03999.pdf)

* [Efficient Defenses Against Adversarial Attacks](https://arxiv.org/pdf/1707.06728v2.pdf)

* [Practical Black-Box Attacks against Machine Learning](https://arxiv.org/pdf/1602.02697.pdf)



### Examples

* [Tensorflow/Cleverhans](https://github.com/tensorflow/cleverhans/tree/master/cleverhans)



## Courses general

* [[UDACITY] Definition of ML](https://classroom.udacity.com/courses/ud262/lessons/3625438937/concepts/6405791890923)

* [[COURSERA] Machine Learning ](https://www.coursera.org/learn/machine-learning)



## Articles General

* [Scraping for Craft Beers: A Dataset Creation Tutorial](http://blog.kaggle.com/2017/01/31/scraping-for-craft-beers-a-dataset-creation-tutorial/)

* [10 misconceptions about Neural Networks](http://www.turingfinance.com/misconceptions-about-neural-networks/)

* [The Black Magic of Deep Learning - Tips and Tricks for the practitioner](https://nmarkou.blogspot.bg/2017/02/the-black-magic-of-deep-learning-tips.html?utm_campaign=Revue+newsletter&utm_medium=Newsletter&utm_source=revue)

* [Productionizing and Deploying Data Science Projects](https://www.anaconda.com/blog/developer-blog/productionizing-and-deploying-data-science-projects/)

* [Applying deep learning to real-world problems](https://medium.com/merantix/applying-deep-learning-to-real-world-problems-ba2d86ac5837)

* [Kaggle Ensembling Guide](https://mlwave.com/kaggle-ensembling-guide/)

* [Compressing deep neural nets](http://machinethink.net/blog/compressing-deep-neural-nets/?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=Deep%20Learning%20Weekly)

* [5 Step Life-Cycle for Neural Network Models in Keras](https://machinelearningmastery.com/5-step-life-cycle-neural-network-models-keras/)

* [Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras](https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/)

* [Stateful LSTM in Keras](http://philipperemy.github.io/keras-stateful-lstm/)

* [Keras plays catch, a single file Reinforcement Learning example](http://edersantana.github.io/articles/keras_rl/])

* [Building a Music Recommender with Deep Learning](http://mattmurray.net/building-a-music-recommender-with-deep-learning/?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=Deep%20Learning%20Weekly)



## Research papers general

* [deeplearning.net research paper reading list](http://deeplearning.net/reading-list/)

* [Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition](http://www.mdpi.com/1424-8220/16/1/115/htm)

* [WaveNet: A generative model for raw audio](https://arxiv.org/pdf/1609.03499.pdf)

* [One-Shot Imitation Learning](https://arxiv.org/pdf/1703.07326.pdf)

*  Connectionist Temporal Classification - ftp://ftp.idsia.ch/pub/juergen/icml2006.pdf

* [Deep Networks With Large Output Spaces](https://arxiv.org/abs/1412.7479)

* [Deep metric learning using Triplet network](https://arxiv.org/abs/1412.6622)

* [Distilling the Knowledge in a Neural Network](https://arxiv.org/pdf/1503.02531.pdf)

* [Intriguing properties of neural networks](https://arxiv.org/pdf/1312.6199v4.pdf)

* [How to Parallelize Deep Learning on GPUs Part 1/2: Data Parallelism](http://timdettmers.com/2014/10/09/deep-learning-data-parallelism/)

* [Attention Is All You Need](https://arxiv.org/pdf/1706.03762.pdf)

* [A simple neural network module for relational reasoning](https://arxiv.org/pdf/1706.01427.pdf)



## Blogs

* [WildML - Artificial Intelligence, Deep Learning, and NLP](http://www.wildml.com/)



## Datasets

 * [Kaggle Datasets](https://www.kaggle.com/datasets)

 * [17 Ultimate Data Science Projects To Boost Your Knowledge and Skill](https://www.analyticsvidhya.com/blog/2016/10/17-ultimate-data-science-projects-to-boost-your-knowledge-and-skills/?utm_content=buffer9424a&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)



## Miscellaneous

* [Essential Cheat Sheets for deep learning and machine learning researchers](https://github.com/kailashahirwar/cheatsheets-ai?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue)

* [Failures of Deep Learning](https://www.youtube.com/watch?v=jWVZnkTfB3c)