Continuing our series of deep learning updates, we pulled together some of the awesome resources that have emerged since our last post. In case you missed it, you can find all past updates here. As always, this list is not comprehensive, so let us know if there’s something we should add, or if you’re interested in discussing this area further.
Mastering the game of Go without human knowledge by Silver et al of DeepMind. AlphaGo implementation that requires no prior knowledge — the system teaches itself. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. Original paper here.
Introducing Gluon: a new library for machine learning from AWS and Microsoft. Microsoft and Amazon take on Google’s TensorFlow and Facebook’s PyTorch with their own new open source deep learning library. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.
Artificial intelligence can say yes to the dress. GANs to generate garment photos for e-commerce. Online fashion tech startup Vue.ai is selling technology that analyzes pieces of clothing and automatically generates an image of the garment on a person of any size, shape, or wearing any kind of shoes (company website here).
Introducing NNVM Compiler: A New Open End-to-End Compiler for AI Frameworks by AWS. We introduce the NNVM compiler, which compiles a high-level computation graph into optimized machine codes. This addresses some of the problems that arise when working across a number of AI frameworks and underlying hardware architectures.
Intel® Nervana™ Neural Network Processors (NNP) Redefine AI Silicon by Naveen Rao. Intel announces a new family of processors designed specifically for artificial intelligence.
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases by Wang et al of NIH. Researchers release medical data set for machine learning. The NIH Clinical Center recently released over 100,000 anonymized chest x-ray images and their corresponding data to the scientific community. NIH compiled the dataset of scans from more than 30,000 patients, including many with advanced lung disease. Original paper here. Data set available on Box here.
Spotify’s Discover Weekly: How machine learning finds your new music by Sophia Ciocca. Explanation of machine learning approach to personalized music recommendations.
Visualizing convolutional neural networks by Justin Francis of University of Alberta. Building convnets from scratch with TensorFlow and TensorBoard.
Gradient descent, how neural networks learn by 3Blue1Brown. Digestable explanation of how gradient descent works (Youtube video).
Protecting Against AI’s Existential Threat by Ilya Sutskever and Dario Amodei of OpenAI. Discussing how to perform AI safe research. How do you create AI that doesn’t pose a threat to humanity? By teaching it to work with humans.
Andrew Ng Has a Chatbot That Can Help with Depression. New chatbot backed by Andrew Ng focused on interactive behavioral therapy to improve mental health.
By Isaac Madan. Isaac is an investor at Venrock (email). If you’re interested in deep learning or there are resources I should share in a future newsletter, I’d love to hear from you. If you’re a machine learning practitioner or student, join our Talent Network here to get exposed to awesome ML opportunities.
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