Flipboard recently reported on an in-house application of deep learning to scale up low-resolution images that illustrates the power and flexibility of this class of learning algorithms.
Under the name of Project Oxford, Microsoft has made available a set of RESTful APIs that aim to make it possible for developers to build apps that feature face recognition, speech processing, and other machine learning algorithms. Part of the Azure portfolio, the new APIs are currently in beta and free to use up to 5,000 call per month.
Facebook has open sourced a number of modules for faster training of neural networks on Torch.
A number of Google researchers and engineers presented their view on the technical debt of using machine learning at a NIPS workshop. They identified different aspects of technical debt and came to the conclusion that without proper care, using machine learning or complex data analysis in your company can induce new kinds of technical debt different from classical software engineering.
Google has announced a new CAPTCHA API which provides a No CAPTHA experience for most users.
Web Summit, one of the largest technology conferences in Europe opened up today. Famous people from the technology and business world are expected to talk, like Peter Thiel, Drew Houston and Anna Patterson.
Microsoft recently announced new machine learning capabilities for Microsoft Azure platform. Developers can also create their own web services and publish them to Azure Marketplace. Microsoft also announced availability of Apache Storm for Azure. Azure Stream Analytics, Data Factory and Event Hubs for Azure were all announced in the past few weeks by Microsoft. In this article we explore moreabout
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.
Splunk’s user conference has drawn to a close. After three days with over 160 sessions ranging from security and operations to business intelligence to even the Internet of Things, the same central theme kept appearing over and over again: the key to Big Data is machine learning.
Nvidia earlier this month released cuDNN, a set of optimized low-level primitives to boost the processing speed of deep neural networks (DNN) on CUDA compatible GPUs. The company intends to help developers harness the power of graphics processing units for deep learning applications.
Microsoft recently announced Azure ML, a machine learning cloud based platform that helps predict future events based on past performance. Microsoft has been using machine learning for years for Bing, Xbox and other products but this is the first time that internal technologies are consumerized and deployed as cloud services. Ersatz Labs is also trying to build a PaaS for Machine Learning.
Domino, a Platform-as-a-Service for data science, enables people to do analytical work using languages such as Python or R in the cloud (EC2).
Recently, Spark graduated from the Apache incubator. Spark claims up to 100x speed improvements over Apache Hadoop over in-memory datasets and gracefully falling back to 10x speed improvement for on-disk performance. Based on Scala, it can run SQL queries and be used directly in R. It provides Machine Learning, Graph database capabilities and other further discussed in the article.
2013 has been rich in announcements for new programs, degrees and grants for aspiring data scientists and Big Data practitioners.
Neural networks have long been an interesting field of research for exploring concepts in machine learning (otherwise known as artificial intelligence). Dr James McCaffrey of Microsoft Research recently gave an introduction to neural networks for those looking to learn more about them in an engaging talk that includes working demo code.