Deep learning is becoming an important AI paradigm for pattern recognition, image/video processing and fraud detection applications in finance. The computational complexity of a deep learning network dictates need for a distributed realization. Our intention is to parallelize the training phase of the network and consequently reduce training time. We have built the first prototype of our distributed deep learning network over Spark, which has emerged as a de-facto standard for realizing machine learning at scale.
This post was written by the team behind DataCamp, the online interactive learning platform for data science. After being dubbed “sexiest job of the 21st Century” by Harvard Business Review, data scientists have stirred the interest of the general public. Many people are intrigued by this job, namely because the name has an interesting […]
This is part 1. part 2 . part 3 . part 4 . My feed (rss) . I've spoken several times about a specific type of architecture I call "Onion Architecture". I've found that it leads to more maintainable applications since it emphasizes separation of concerns
A fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning
H2O makes Hadoop do Math! H2O scales statistics, machine learning and math over Big Data. H2O keeps familiar interfaces like R, Excel & JSON so that big data enthusiasts and experts can explore, munge, model and score data sets using a range of simple to advanced algorithms.
Natural language isn't that great for searching. When you type a search query into Google, you miss out on a wide spectrum of human concepts and human emotions. Queried words have to be present in the web page, and then those pages are ranked according to the number of inbound and outbound links. That's great for filtering out the cruft on the internet -- and there's a lot of that out there. What it doesn't do is understand the relationships between words and understand the similarities or dissimilarities.
When you are on the bleeding edge of scale like Facebook is, you run into some interesting problems. As of 2008 Facebook had over 800 memcached servers supplying over 28 terabytes of cache. With those staggering numbers it's a fair bet to think they've seen their share of Dr. House worthy memcached problems.
At TechEd Europe 2014, Microsoft announced the preview of Azure Stream Analytics. Stream Analytics is a real-time event processing engine that helps uncover insights from devices, sensors, infrastructure, applications, and data. With out-of-the-box integration to Event Hubs, the combined solution can both ingest millions of events as well as do analytics to better understand patterns, power a dashboard, detect anomalies, and kick off an action while data is being streamed in real-time.
In-memory big data has come of age. Spark platform with it’s elegant API and architecture has captured developer’s hearts. Machine learning as an API for big data is just as real. R and predictive analytics on Big Data has become the center of the space. H2O has established a leadership in scalable ML having focused over the past two years. Spark captured developer’s hearts and minds of developers at the same time.
Sparkling Water brings together best of the both worlds!
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Twitter’s engineering group, known for various contributions to open source from streaming MapReduce to front-end framework Bootstrap recently announced open sourcing an algorithm that can efficiently recommend content. LinkedIn also open sourced a Machine Learning library of its own, ml-ease. In this article we present the algorithms and what they mean for the open source community.
Today, we announced an exciting set of joint initiatives with Microsoft, including:
Extending Docker to Windows with Docker Engine for Windows ServerMicrosoft’s support of Docker’s open orchestration APIsIntegration of Docker Hub with Microsoft Azure, andCollaboration on the multi-Docker container model, including support for applications consisting of both Linux and Windows Docker containers
I’d like to provide some context for this announcement, and why we are so excited.