Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Source: Caffe | Deep Learning Framework

Tensors and Dynamic neural networks in Python with strong GPU acceleration. PyTorch is a deep learning framework that puts Python first. We are in an early-release Beta. Expect some adventures. Source: PyTorch

Machine learning is cropping up more and more in research papers – does it work? Source: From drugs to galaxy hunting, AI is elbowing its way into boffins’ labs • The Register

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss Read more about Image-to-Image Translation with Conditional Adversarial Networks[…]

ETSI is pleased to announce the creation of the Industry Specification Group ‘Experiential Network Intelligence’ (ISG ENI). The purpose of the group is to define a Cognitive Network Management architecture that is based on the “observe-orient-decide-act” control model. It uses AI (Artificial Intelligence) techniques and context-aware policies to adjust offered services based on changes in Read more about New ETSI group on improving operator experience using Artificial Intelligence[…]

Gamalon uses a technique that it calls Bayesian program synthesis to build algorithms capable of learning from fewer examples. Bayesian probability, named after the 18th century mathematician Thomas Bayes, provides a mathematical framework for refining predictions about the world based on experience. Gamalon’s system uses probabilistic programming—or code that deals in probabilities rather than specific Read more about AI Software Juggles Probabilities to Learn from Less Data[…]

Graph Engine – Open Source Microsoft Graph Engine is a distributed in-memory data processing engine, underpinned by a strongly-typed in-memory key-value store and a general distributed computation engine. This repository contains the source code of Graph Engine and its graph query language — Language Integrated Knowledge Query (LIKQ). LIKQ is a versatile graph query language Read more about Microsoft Graph Engine goes open source on github[…]

As described by Hadley Wickham, tidy data has a specific structure: each variable is a column each observation is a row each type of observational unit is a table This means we end up with a data set that is in a long, skinny format instead of a wide format. Tidy data sets are easier Read more about The Life-Changing Magic of Tidying Text in an R package[…]

text2vec is an R package which provides an efficient framework with a concise API for text analysis and natural language processing (NLP). Goals which we aimed to achieve as a result of development of text2vec: Concise – expose as few functions as possible Consistent – expose unified interfaces, no need to explore new interface for Read more about text2vec[…]

Source: multilabel datasets (for mulan) UCI Center for Machine Learning and Intelligent Systems Machine Learning Repository Netflix Prize dataset

Initially used to improve the experience for visually impaired members of the Facebook community, the company’s Lumos computer vision platform is now powering image content search for all users. This means you can now search for images on Facebook with key words that describe the contents of a photo, rather than being limited by tags Read more about Facebook’s AI unlocks the ability to search photos by what’s in them[…]

As the “Brains vs. Artificial Intelligence: Upping the Ante” poker competition nears its halfway point, Carnegie Mellon University’s AI program, Libratus, is opening a lead over its human opponents — four of the world’s best professional poker players.One of the pros, Jimmy Chou, said he and his colleagues initially underestimated Libratus, but have come to Read more about CMU AI Is Tough Poker Player[…]

A bunch of highly funded researchers Source: The Google Brain Team Publications subdivided into subjects Machine Intelligence publications subset Big Picture does a lot of high level visualisation of datasets and their relationships

When we look very closely at images generated by neural networks, we often see a strange checkerboard pattern of artifacts. It’s more obvious in some cases than others, but a large fraction of recent models exhibit this behavior. Mysteriously, the checkerboard pattern tends to be most prominent in images with strong colors. What’s going on? Read more about Deconvolution and Checkerboard Artifacts — Distill[…]

A popular method for exploring high-dimensional data is something called t-SNE, introduced by van der Maaten and Hinton in 2008. The technique has become widespread in the field of machine learning, since it has an almost magical ability to create compelling two-dimensonal “maps” from data with hundreds or even thousands of dimensions. Although impressive, these Read more about How to Use t-SNE Effectively — Distill[…]

Recurrent neural networks are one of the staples of deep learning, allowing neural networks to work with sequences of data like text, audio and video. They can be used to boil a sequence down into a high-level understanding, to annotate sequences, and even to generate new sequences from scratch! Source: Attention and Augmented Recurrent Neural Read more about Attention and Augmented Recurrent Neural Networks — Distill[…]

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. Source: Deep Learning

Neural Networks and Deep Learning is a free online book. The book will teach you about: Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data Deep learning, a powerful set of techniques for learning in neural networks Neural networks and deep learning currently provide the best solutions to Read more about Neural networks and deep learning[…]

Several interactive visualizations of a generative model of handwriting. Some are fun, some are serious. Source: Experiments in Handwriting with a Neural Network — Distill

Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Indeed, in the context of TensorFlow, it’s natural to view tensors (or slices of tensors) as points in space, so almost any TensorFlow system will naturally give rise to various embeddings. To learn more about embeddings and how to train Read more about TensorBoard: Embedding Visualization for Tensorflow[…]

DeepMind Lab is a fully 3D game-like platform tailored for agent-based AI research. It is observed from a first-person viewpoint, through the eyes of the simulated agent. Scenes are rendered with rich science fiction-style visuals. The available actions allow agents to look around and move in 3D. The agent’s “body” is a floating orb. It Read more about Open-sourcing DeepMind Lab[…]

We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications. Universe allows an AI agent to use a computer like a human does: by looking at screen pixels and operating a virtual keyboard and mouse. We must train AI systems on Read more about OpenAI Universe allows your AI to train on games, browsers by looking at screen pixels. Uses Gym (also OSS) for algo devs[…]