Recommended this week
- Graph Auto-Encoders — Tensorflow implementation of Variational Graph Auto-Encoders.
- Mxnet-memonger — Sublinear memory optimization for deep learning, reduce GPU memory cost to train deeper nets.
- Facets — Open Source Visualization Tool for Machine Learning Training Data from Google Developers.
- “Learning Macromanagement in StarCraft from Replays using Deep Learning” — outperforms the game’s built-in Terran bot.
- “Self Adversarial Training for Human Pose Estimation”. A deep ConvNet model learns the structure and configuration of human body parts via adversarial training.Achieves state-of-the-art results on LSP, MPII, and LIP datasets.
- “Enhanced Deep Residual Networks for Single Image Super-Resolution” –appears in CVPR 2017 workshop. Best paper award of the NTIRE2017 workshop, and the winners of the NTIRE2017 Challenge on Single Image Super-Resolution.
- “The Reversible Residual Network: Backpropagation Without Storing Activations”. A variant of ResNets where each layer’s activations can be reconstructed exactly from the next layer’s, so the activation storage requirements are independent of depth.
- “Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning”.
- “The Devil is in the Decoder”. Paper from Google presents an extensive comparison of a variety of decoders for a variety of pixel-wise prediction tasks.
- “Deep Bilateral Learning for Real-Time Image Enhancement”. Paper from Google and MIT researchers processes high-resolution images on a smartphone in milliseconds.
- Curated list of R tutorials for Data Science, NLP and Machine Learning
- Machine Learning Crash Course by UC Berkeley
- What are some new and exciting areas in adversarial machine learning research? Answered by Ian Goodfellow.
- “You and Your Research” by Richard Hamming. Transcription of the Bell Communications Research Colloquium Seminar 7 March 1986.
- “Learning to Learn” by Berkeley AI PhD student Chelsea Finn.
- A curated list of super-resolution benchmarks and resources for single image super-resolution algorithms.
- Using Machine Learning to Predict Value of Homes On Airbnb by Robert Chang.
- Neuroevolution: A different kind of deep learning. The quest to evolve neural networks through evolutionary algorithms.
- SciPy 2017: Scientific Computing with Python Conference. Videos of the conference talks and tutorials.
- Parallel Programming in R and Python. Shows you how to utilize multi-core, high-memory machines to dramatically accelerate your computations in R and Python, without any complex or time-consuming setup. By Domino Data Lab.
- Data Science in Practice. Course by Professor Bradley Voytek UC San Diego.
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