Sharing some of the latest research, announcements, and resources on deep learning. By Isaac Madan ( email ) 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 2016. 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. May April part 2 April part 1 March part 1 February 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 Announcements by Gehring . Facebook team demonstrates using a novel convolutional neural network (CNN) approach for language translation that achieves state-of-the-art accuracy at nine times the speed of recurrent neural systems. FAIR sequence modeling toolkit (fairseq) available on GitHub so researchers can build custom models for translation, text summarization, etc. GitHub repo . Original paper . Convolutional Sequence to Sequence Learning et al here here by Engaget. Relevant announcements by NVIDIA pertaining to AI & machine learning hardware advancements. Video. NVIDIA GPU Tech Conference 2017 Highlights in 12 Minutes by Google. 1,000 Cloud TPUs for the world’s top researchers to accelerate deep learning research. TensorFlow Research Cloud by Jeff Dean and Urs Hölzle. Build and train machine learning models on our new Google Cloud TPUs We’re excited to announce that our second-generation Tensor Processing Units (TPUs) are coming to Google Cloud to accelerate a wide range of machine learning workloads, including both training and inference. We call them Cloud TPUs, and they will initially be available via Google Compute Engine. by Baidu. Human speech generation with less training data. Deep Voice 2 It can learn the nuances of a person’s voice with just half an hour of audio, and a single system can learn to imitate hundreds of different speakers ( article ). Research by Holden . University of Edinburgh researchers demonstrate using neural networks to more realistically animate the way characters moving in a real-time game environment, trained on a locomotion dataset of movement in virtual scenes. Original paper . Phase-Functioned Neural Networks for Character Control et al here by OpenAI. Robots that Learn We’ve created a robotics system, trained entirely in simulation and deployed on a physical robot, which can learn a new task after seeing it done once. by Quoc Le of Google. Google demonstrates a reinforcement learning approach to automate the design of machine learning models, making them more accessible. Using Machine Learning to Explore Neural Network Architecture et al The process of manually designing machine learning models is difficult because the search space of all possible models can be combinatorially large. by Haozhi Qi . fully convolutional end-to-end solution for instance segmentation, which won the first place in COCO segmentation challenge 2016. See sample images of their instance segmentation at work . Original paper . Fully Convolutional Instance-aware Semantic Segmentation et al here here by Lijun Wu . via an adversarial training architecture, inspired by recent successes of generative adversarial networks. Adversarial Neural Machine Translation et al In this paper, we study a new learning paradigm for Neural Machine Translation (NMT). Instead of maximizing the likelihood of the human translation as in previous works, we minimize the distinction between human translation and the translation given by a NMT model, by Abigail See of Stanford. Enhancing abstractive, automatic text summarization via novel deep neural network architecture. Original paper . Taming Recurrent Neural Networks for Better Summarization In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. here Resources by Andrej Karpathy of OpenAI. Digs into the questions: AlphaGo, in context “to what extent is AlphaGo a breakthrough?”, “How do researchers in AI see its victories?” and “what implications do the wins have?” by Fei-Fei Li, chief scientist of AI/ML at Google Cloud, associate professor of computer science at Stanford. An overview of AI and it’s potential in image & video recognition by Eugenio Culurciello. Describes novel deep neural networks suitable for unsupervised learning, such as generative ladder networks, recursive ladder networks, and predictive coding networks, and their relation to generative adversarial networks. Paper on deep predictive coding networks . A new kind of deep neural networks here also by Eugenio Culurciello. Overview of unsupervised learning methods spanning general concepts, auto encoders, clustering, generative models, and more. Navigating the Unsupervised Learning Landscape by OpenAI. Open-source software for robot simulation, integrated with OpenAI Gym. Roboschool also by OpenAI. Baselines: high-quality implementations of reinforcement learning algorithms A set of high-quality implementations of reinforcement learning algorithms. These algorithms will make it easier for the research community to replicate, refine, and identify new ideas, and will create good baselines to build research on top of. by David Venturi. Overview & reviews of various deep learning courses. Dive into Deep Learning with 15 free online courses by Igor Bobriakov. Overview of Python libraries for basic data science, visualization, machine learning, NLP, data mining, and stats. Top 15 Python Libraries for Data Science in 2017 Tutorials & Data by Jeremy Stanley of Instacart. 3 Million Instacart Orders, Open Sourced This anonymized dataset contains a sample of over 3 million grocery orders from more than 200,000 Instacart users. We hope the machine learning community will use this data to test models for predicting products that a user will buy again, try for the first time or add to cart next during a session. by Google. Video. Effective TensorFlow for Non-Experts (Google I/O ’17) In this talk, you will learn how to use TensorFlow effectively. TensorFlow offers high level interfaces like Keras and Estimators, which can be used without being an expert. This talk will show how to implement complex machine learning models and deploy them on any platform that supports TensorFlow. by Slav Ivanov. Walk-thru of building a desktop for deep learning from scratch. The $1700 great Deep Learning box: Assembly, setup and benchmarks 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.
