Sharing some of the latest research, announcements, and resources on deep learning. By Isaac Madan 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, here are our past updates: , , , , , , , and the of 20+ resources we outlined in April. As always, this list is not comprehensive, so if there’s something we should add, or if you’re interested in discussing this area further. November September part 2 & October part 1 September part 1 August part 2 , August part 1 July part 2 July part 1 June original set let us know Research by Chao Yang of USC. A novel deep learning approach to filling in large holes in images effectively. GitHub repo . High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis et al here by Dahl of Google. Researchers at Google Brain demonstrates a deep learning method of generating higher resolution images from lower resolution, pixelated images. Pixel Recursive Super Resolution et al by Yunjey Choi. TensorFlow implementation of — generating novel images of previously unseen entities, while preserving their identity. Domain Transfer Network implemention Unsupervised Cross-Domain Image Generation Announcements by University of Montreal, organized by Yoshua Bengio. A conference June 26th to July 4th, 2017. Applications due March 20th, apply . Deep Learning Summer School and Reinforcement Learning Summer School aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research. here by Joseph Spisak of AWS. Amazon announces that you can now run deep learning in the cloud via Ubuntu instances that have popular frameworks pre-installed, like TensorFlow, Caffe, etc. The AWS Deep Learning AMI, Now with Ubuntu by Google. At the TensorFlow Developer Summit in mid-February in Mountain View, Google announces the official 1.0 release of TensorFlow. The framework is now being used in over 6000 open source repositories. Announcing TensorFlow 1.0 Resources by Dev Nag, CTO of Wavefront. An explanation of GANs and a simple way to get started with them. GitHub repo . Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch) here by Massimiliano Patacchiola of Plymouth University. An in-depth explanation of reinforcement learning, with an accompanying GitHub repo for code and resources discussed — found . Dissecting Reinforcement Learning here by Matt Harvey. Using Udacity self-driving car simulator to train a generalized steering model for a car in under 100 lines of code. Training a deep learning model to steer a car in 99 lines of code by Mike Waldron of Data Science Central. Brief explanations of common deep learning terms like and . Also helpful from the same site, . 10 Deep Learning Terms Explained in Simple English backpropagation gradient descent 15 Deep Learning Tutorials by Yoshua Bengio (video). Exploring the path forward to human-level AI at the Beneficial AI 2017 Conference organized by the Future of Life Institute. Also, by Yann LeCun at the same conference. Creating Human-Level AI A Path to AI by Yuxi Li. Overview of recent advancements in deep reinforcement learning, alongside background explanation. Deep Reinforcement Learning: An Overview by Martin Gorner of Google Cloud Platform. Course for developers on deep learning basics and leveraging TensorFlow. Learn TensorFlow and deep learning, without a PhD by Eren Golge. Quora launched their first publicly available in late January for developers to get a sense of the challenges in building a knowledge-sharing network. This exploration takes a deep learning approach to identifying duplicate questions. Duplicate Question Detection with Deep Learning on Quora Dataset dataset by MIT (video). Lecture 4 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017 at MIT. Course website . Lecture 4 slides . Recurrent Neural Networks for Steering Through Time here here By . Isaac is an investor at Venrock ( ). If you’re interested in deep learning, we’d love to . Isaac Madan email hear from you is a newsletter of entrepreneurial ideas & perspectives by investors, operators, and influencers. Requests for Startups ** **❤” Please tap or click “︎ to help to promote this piece to others.
