awesome-data-science
awesome-data-science copied to clipboard
Personal collection of interesting links & research papers related to data science (particularly in the area of Deep Learning)
Awesome Data Science
The below list is by no means an exhaustive list of data science, it is a set of my personal bookmarks of interesting reads, primarily related to neural networks. It also is heavily skewed towards resources that are useful for source{d}
Online books
- Deep Learning by Ian Goodfellow, Aaron Courville, and Yoshua Bengio
- Neural Networks and Deep Learning by Michael Nielsen
Online Resource Collections
- Tutorials for TensorFlow The best neural network tutorials I've come across
- Christopher Olah's Blog One of the best resources for truly understanding neural networks
- Deep Learning for NLP resources
- What NLP problems has deep learning or neural networks been applied to successfully?
- Speech and Natural Language Processing
- How to actually learn data science
- GitXiv — Collaborative Open Computer Science
Online courses
- CS224d: Deep Learning for Natural Language Processing
- Neural networks class - Université de Sherbrooke
- NVIDIA Deep Learning Courses
- YC HN thread with interesting courses
Research papers (incl. arXiv)
- Semantics, Representations and Grammars for Deep Learning; 2015 - David Balduzzi
- Towards Neural Network-based Reasoning; 2015 - Baolin Peng, Zhengdong Lu, Hang Li, Kam-Fai Wong
- Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank; 2013 - Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng and Christopher Potts
- Text Understanding from Scratch; 2015 - Xiang Zhang, Yann LeCun
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting; 2013 - Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
- Learning To Execute; 2014 - Wojciech Zaremba, Ilya Sutskever
- When Are Tree Structures Necessary for Deep Learning of Representations?; 2015 - Jiwei Li, Minh-Thang Luong, Dan Jurafsky, Eudard Hovy
- Distributed Representations of Sentences and Documents; 2014 - Quoc Le, Tomas Mikolov
- Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets; 2015 - Armand Joulin, Tomas Mikolov
- Multi-View Learning of Word Embeddings via CCA; 2011 - Paramveer S. Dhillon, Dean Foster, Lyle Ungar
- Is deep learning really necessary for word embeddings?; 2013 - Remi Lebret, Joel Legrand, Ronan Collobert
- Intriguing properties of neural networks; 2013 - Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus
- Building Program Vector Representations for Deep Learning; 2014 - Lili Mou, Ge Li, Yuxuan Liu, Hao Peng, Zhi Jin, Yan Xu, Lu Zhang
- Who Wrote This Code? Identifying the Authors of Program Binaries; 2011 - Nathan Rosenblum, Xiaojin Zhu, Barton P. Miller
Interesting articles
- The Unreasonable Effectiveness of Recurrent Neural Networks
- Wolfram Language Artificial Intelligence: The Image Identification Project
- Deep Learning vs Probabilistic Graphical Models vs Logic
- A Step by Step Backpropagation Example
- A Neural Network in 11 lines of Python (Part 1)
- Suddenly, a leopard print sofa appears
- Hacker's guide to Neural Networks
- Auto-Generating Clickbait With Recurrent Neural Networks
- What a Deep Neural Network thinks about your #selfie
- Neural Networks, Types, and Functional Programming
- Composing Music With Recurrent Neural Networks
- Image Scaling using Deep Convolutional Neural Networks
- The neural networks behind Google Voice transcription
- 26 Things I Learned In The Deep Learning Summer School
- Growing Pains for Deep Learning
- Understanding Natural Language Deep Neural Networks Using Torch
- Generating Magic cards using deep, recurrent neural networks
- Google voice search: faster and more accurate
- How Google Translate squeezes deep learning onto a phone
- Evolution of Deep learning models
- AMA Geoffrey Hinton
- Baidu explains how it’s mastering Mandarin with deep learning
- Calculus on Computational Graphs: Backpropagation
- Geoffrey Hinton on max pooling (reddit AMA)
- Algorithms of the Mind
- The elastic brain
- How does word2vec work?
- A conversation with Sussman on AI and asynchronous programming
- The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near
- Learning from the best
- Data has a shape
- Petabyte-Scale Text Processing with Spark
- Document Clustering With Python
- A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
- Faster deep learning with GPUs and Theano
Interesting news articles
- You're Using Neural Networks Every Day Online—Here's How They Work
- Five crazy abstractions my Deep Learning word2vec model just did
- Google DeepMind Teaches Artificial Intelligence Machines to Read
- Google's AI can learn to play video games
- How Google’s New Photos App Can Tell Cats From Dogs
- Neural network chip built using memristors
- Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter
- Biggest Neural Network Ever Pushes AI Deep Learning
- The Yahoo Behind Fresh Deep Learning Approaches at Flickr
- Neuroscience: The Man Who Saw Time Stand Still
Interesting videos
- Interview with Google's AI and Deep Learning 'Godfather' Geoffrey Hinton
- Why Google believes nothing will be lost in translation
- List of talks on Deep Learning