- Google (https://research.googleblog.com/)
- Nvidia (https://developer.nvidia.com/blog)
- MIT (http://news.mit.edu/topic/machine-learning)
- TensorFlow (https://www.tensorflow.org/versions/r0.9/api_docs/index.html)
- Scikit-learn (http://scikit-learn.org/stable/documentation.html)
- Numpy (https://docs.scipy.org/doc/numpy/reference/routines.html)
- Pandas (http://pandas.pydata.org/pandas-docs/version/0.18.1/)
- NIPS (http://papers.nips.cc)
- ArXiv (https://arxiv.org/)
- Best practices (https://www.microsoft.com/en-us/research/wp-content/uploads/2003/08/icdar03.pdf)
- AlexNet (https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
- GoogLeNet (http://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf)
- Inception-v2 (https://arxiv.org/pdf/1512.00567.pdf)
- ResNet (https://arxiv.org/pdf/1512.03385v1.pdf)
- DeepFace (https://research.facebook.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/)
- A Neural Conversational Model (https://arxiv.org/pdf/1506.05869.pdf)
- Neural Conversation Model (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/1510.03055v25B15D-2.pdf)
- Persona-Based Neural Conversation Model (https://arxiv.org/pdf/1603.06155.pdf)
- CS231n stanford (http://cs231n.stanford.edu/)
- List of courses (https://github.com/open-source-society/data-science)
- Stanford course (http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning)
- Neural Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/)
- Deep Learning (http://www.deeplearningbook.org/)
- Collection of questions (http://analyticscosm.com/machine-learning-interview-questions-for-data-scientist-interview/)
- Interview advices (https://alyaabbott.wordpress.com/2014/10/01/how-to-ace-a-data-science-interview/)
- Where to start (http://ofir.io/How-to-Start-Learning-Deep-Learning/)
- Free books (https://hackerlists.com/free-machine-learning-books/)