Physicists typically think they “need philosophers and historians of science like birds need ornithologists,” the Nobel laureate David Gross told a roomful of philosophers, historians and physicists last week in Munich, Germany, paraphrasing Richard Feynman.
But desperate times call for desperate measures. Fundamental physics faces a problem, Gross explained — one dire enough to call for outsiders’ perspectives. Flink Forward » BigPetStore: A comprehensive blueprint for Apache Flink. Big Data processing frameworks like Apache Flink make it easy to write distributed programs, but it can be difficult to go from understanding simple tutorial applications to writing scalable, production-ready code.
Art of Visualising Software… by Simon Brown. A short guide to creating a shared vocabulary and simple software architecture diagrams using my C4 software architecture model.
This book focusses on the visual communication of software architecture. You'll notice that the title of this book includes the word "art". I've seen a number of debates over the years about whether software development is a craft or an engineering discipline. Best Deep Learning Resources — Digital Mind. Advanced Techniques for concurrency and memory management. 26 Things I Learned in the Deep Learning Summer School. In the beginning of August I got the chance to attend the Deep Learning Summer School in Montreal.
It consisted of 10 days of talks from some of the most well-known neural network researchers. Patterns for real-time stream processing — Martin Kleppmann’s talks. A talk at Crunch Conference, Budapest, Hungary, 30 Oct 2015 Abstract You have some streams of data, such as user activity on a website, or sensor readings from devices.
Now you want to process the data and make it useful with low latency: for example, generating real-time recommendations, detecting abuse, filtering spam or predicting demand. The O-Ring Theory of DevOps. Spark is dead – long live …. ? Hadoop is, largely, synonymous with Big Data.
This is because Hadoop has the potential to alter how we analyse data – by providing relatively cheap, highly scalable storage and analytics capabilities that allow business to ask, and answer, questions that were previously unanswerable. The promise of Big Data has raised the profile of data at C-level, created new roles such as the Chief Data Officer and is driving demand for previously unloved disciplines such as data governance. In spite of the interest, and the hype, Gartner Research shows that only about 40% of companies have made serious investments in Hadoop yet – with others expected to begin within the next few years.
One factors that hinders adoption: The Hadoop framework is still evolving. Graphs and Natural Language Processing « Cambridge Extra at LINGUIST List. By Robert Driver, on November 10th, 2015 Blog post written by Vivi Nastase based on the special issue ‘Graphs and Natural Language Processing’ in the journal Natural Language Engineering. Graph structures naturally model connections. In natural language processing (NLP) connections are ubiquitous, on anything between small and web scale: between words — as structural/grammatical or semantic connections; between concepts in ontologies or semantic repositories; between web pages; between entities in social networks. Such connections are relatively obvious and the parallel with the graph structures straight-forward. Nuit Blanche: The Big List of Deep Learning Toolkits - implementation - A Practical Guide to Building Recommender Systems.
Gmail. S, meet Docker! Ring's video doorbell let me banish unwanted visitors. Ring itself is a 12.65 x 6.17 x 2.21 cm box that mounts either onto your doorframe or an adjacent wall, and it's worth mentioning up front that it's pretty noticeable.
It's basically split into two halves, with the top housing the infra-red camera, motion sensor, microphone and speaker. Simple end-to-end TensorFlow examples. I’ve been reading papers about deep learning for several years now, but until recently hadn’t dug in and implemented any models using deep learning techniques for myself.
To remedy this, I started experimenting with Deeplearning4J a few weeks ago, but with limited success. I read more books, primers and tutorials, especially the amazing series of blog posts by Chris Olah and Denny Britz. Asynchronous Complex Analytics in a Distributed Dataflow Architecture. Asynchronous Complex Analytics in a Distributed Dataflow Architecture – Gonzalez et al. 2015 Here’s a theme we’ve seen before: the programming model offered by large scale distributed systems doesn’t always lend itself to efficient algorithms for solving certain classes of problems.
In today’s paper, Gonzalez et al. examine the growing gap between efficient machine learning algorithms exploiting asynchrony and fine-grained communication, and commodity distributed dataflow systems (Hadoop and Spark) that are optimized for coarse-grained models. Tensorflow Tutorial — Part 2. Tensorflow Tutorial — Part 2 In the previous Part 1 of this tutorial, I introduced a bit of TensorFlow and Scikit Flow and showed how to build a simple logistic regression model on Titanic dataset.
In this part let’s go deeper and try multi-layer fully connected neural networks, writing your custom model to plug into the Scikit Flow and top it with trying out convolutional networks. Multi-layer fully connected neural network. Data Science Society - 04.05.15 - Deep Learning for NLP. After a short spring break Data Science Society meets skill sharing @ betahaus with another talk by a SoundCloud alumnus. Topic: Deep Learning for NLP Speaker: Ivan Vergiliev Resume: Ivan has a stunning track record at some of the hottest tech ventures around the globe.