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. June ( part 1 ) May April ( part 1 , part 2 ) March part 1 February November September part 2 & October part 1 September part 1 August ( part 1 , part 2 ) July part 1 part 2 ), June original set let us know Research & Announcements by Spring and Srivastava of Cornell. News article . Scalable and Sustainable Deep Learning via Randomized Hashing Rice University computer scientists have adapted a widely used technique for rapid data lookup to slash the amount of computation — and thus energy and time — required for deep learning. “This applies to any deep learning architecture, and the technique scales sublinearly, which means that the larger the deep neural network to which this is applied, the more the savings in computations there will be,” said Shrivastava. here by DeepMind. Relational reasoning is the process of drawing conclusions about how things are related to one another, and is central to human intelligence. These papers show promising approaches to understanding the challenge of relational reasoning. Original papers and . A neural approach to relational reasoning A key challenge in developing artificial intelligence systems with the flexibility and efficiency of human cognition is giving them a similar ability — to reason about entities and their relations from unstructured data. here here Resources by Rasmus Rothe of Merantix. A must-read on key learnings when using deep learning in the real world. Discussion of the value of pre-training, caveats of real-world label distributions, and understanding black box models. Applying deep learning to real-world problems by Preferred Networks. Compatible with, or a drop-in replacement for, Numpy. GitHub repo . CuPy An open-source matrix library accelerated with NVIDIA CUDA. here by The AI Conference. Various news articles, academic papers, and datasets shared by folks involved in and enthusiastic about AI. (h/t ) Speaker Resources 2017 Michelle Valentine by Eugenio Culurciello. An in-depth overview & history or neural network architectures in the context of deep learning, spanning LeNet5, AlexNet, GoogLeNet, Inception, and a discussion of where things are headed in the future. Original paper . Neural Network Architectures here by Sebastian Raschka. , including classifiers, autoencoders, GANs, and more. The broader repo for Sebastian’s book is also useful, . Model Zoo A collection of standalone TensorFlow models in Jupyter Notebooks here Tutorials & Data by Google. A TensorFlow recurrent neural network model for teaching machines to draw. Overview of the model and how to use it. Described in greater depth by Google and . Sketch-RNN: A Generative Model for Vector Drawings here here by Edwin Chen. An overview of long short-term memory networks, and a tutorial on their use. Exploring LSTMs It turns out LSTMs are a fairly simple extension to neural networks, and they’re behind a lot of the amazing achievements deep learning has made in the past few years. So I’ll try to present them as intuitively as possible — in such a way that you could have discovered them yourself. by Mapillary. (h/t ) Vistas Dataset Free for research, the MVD is the worlds largest manually annotated semantic segmentation training data set for street level imagery. Primarily being used to train deep neural nets focused on object detection, semantic segmentation, and scene understanding for ADAS and autonomous. Andrew Mahon By . Isaac is an investor at Venrock ( ). 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. Isaac Madan email 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.
