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PredictionIO Open Source Machine Learning Server

PredictionIO Open Source Machine Learning Server
Related:  WineooxMachine learning

Sematext Products Overview Logsene - Centralized Log Management and Analytics Logsene is an affordable centralized log management and analytics solution available in the cloud and on premises. You can send your logs to Logsene for indexing and make them instantly searchable with nothing to install or maintain. It exposes syslog (rsyslog) and Elasticsearch API endpoints, consumes logs from syslog, Logstash, Flume, Fluentd, etc., It exposes multiple user interfaces, including Kibana. It integrates with SPM to correlate logs with performance metrics, alerts, anomalies, and events. Site Search Analytics (SSA) Sematext Site Search Analytics (SSA) is the enterprise-class, cloud-based, search vendor-neutral Site Search Analytics SaaS. Businesses use Sematext Site Search Analytics to collect and analyze user search behavior data, clickstream data, and search-related transactions. Search AutoComplete / Solr Suggester Query Relaxer Query Relaxer is a Solr component that improves search experience. Related Searches

20 Resources for Teaching Kids How to Program & Code Isn't it amazing to see a baby or a toddler handle a tablet or a smart phone? They know how technology works. Kids absorb information so fast, languages (spoken or coded) can be learned in a matter of months. Recently there has been a surge of articles and studies emerging about teaching kids to code. We live in a "Back to the Future" movie. Programming is viewed as a strict logical stream only available to brainiacs. It can also teach parity in technology fields when girls are brought up thinking they are just as good at math and sciences as boys. Here are a 20 resources you can use to introduce and teach children about coding and programming: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. (Image credits: Flickr User Vauvau)

Crawl Anywhere Rhizomatic Learning - The community is the curriculum Doing this course I've put together a blog post to give you a sense of 'where' the course is happening and what you might like to do as part of it. READ THIS FIRST = Your unguided tour of Rhizo14 Why might this course be for you? Rhizomatic learning is a story of how we can learn in a world of abundance – abundance of perspective, of information and of connection. What happens if we let that go? Opening blog posts thoughts Slightly more complex intro Rhizomatic Learning posits, among other things, that the community is the curriculum. Course starts January 14th. tweet at #rhizo14 OSC — We build search engines, with a strong focus on application development and data architecture Search Application Development Building large search applications can be daunting. Our development teams can turn your search dreams into a reality. Full-text Search Applications We build search engines for large content repositories with specialized search requirements. Discovery Interfaces Rich discovery applications allow you to organically navigate your content and see relationships between items. Data Visualization Simple data is useless if you cannot visualize the trends and relationships between the points. Data Integration We can install and optimize solutions for managing your big data and analytics needs. Cloud Computing We specialize in managing distributed computing environments for data storage and ingestion. Data Ingestion From patent data to corporate file systems we have proven our ability to build aggregate search databases. Data Science Too much data, not enough answers? Search Engine Triage Struggling with a tough search problem? Search Architecture Assessments Relevancy Tuning

Dave's Educational Blog | Education, post-structuralism and the rise of the machines I was asked by the excellent Sheryl Nussbaum-Beach to speak to her PLP class about MOOCs, and, while we had what i thought was an excellent forty minute chat, there were tons of comments that i never had the chance to address. As i look over the questions they asked, I see that in answering their questions i have a chance to lay out many of the thoughts that I have had about MOOCs while they have been all the rage here on the internet in the last few weeks. I opened the discussion with a quick personal intro to my contribution to the MOOC discussion and then we moved to Q & A. Intro Edtechtalk and community – 2005 In 2005 Jeff Lebow and I started edtechtalk. What i discovered was that, simply by engaging in random discussions with new people we happened upon – I was learning. Rhizomes 2006 This lead me to new ideas about what it meant to learn and what it meant to know. Q & A. Pete: Does the MOOC really have to be “massive”? I see it as a win. Pete: Is/Will there be an accredited MOOC?

Online Applications « LTU – The Image Recognition API Pureshopping – Leading shopping portal dedicated to beauty, fashion and decor Formerly known as, is a site for fashion, beauty and home decor as well as an engine of inspiration offering new ideas, innovative window-shopping and tools for finding desired products. provides a convenient, engaging and relevant shopping experience by making it easy for users to find products they are looking for as well as providing buying guides, product descriptions, information on trends and sections devoted to major brands. Shoppers are directed to partner sites to purchase the products found on AndycOt – smart marketplace for smart collectors andycOt revolutionizes the online auctions world through image recognition technology powered by LTU technologies.

Pourquoi le machine learning cartonne dans la Silicon Valley Pourquoi et comment Facebook ou Google se servent de l'apprentissage automatique ? Peut-il servir à d'autres acteurs ? Des techniques pas nouvelles, mais en plein boom. L'apprentissage automatique ou "machine learning" est en vogue dans la Silicon Valley. Le sondage souvent utilisé comme base du machine learning Dans les deux cas, Google et Facebook ont conçu, dans le cadre d'un sondage, une série de questions qui devaient servir à déterminer la qualité d'un contenu. Sondage, arbre de décision : des éléments que l'on retrouve souvent dans le machine learning. Ces sondages sont souvent utilisés pour le machine learning : ils pourront même pouvoir en être le socle – du moins pour ce que les spécialistes appellent plus précisément "l'apprentissage supervisé". La machine pourra ensuite retenir les variables discriminantes, c'est-à-dire des critères, et les appliquer. De vastes champs d'applications Facebook ou Google ne sont pas les seuls à utiliser le machine learning.

Strategies for Two-Sided Markets If you listed the blockbuster products and services that have redefined the global business landscape, you’d find that many of them tie together two distinct groups of users in a network. Case in point: What has been the most important innovation in financial services since World War II? Answer: almost certainly the credit card, which links consumers and merchants. Products and services that bring together groups of users in two-sided networks are platforms. Two-sided networks can be found in many industries, sharing the space with traditional product and service offerings. In traditional value chains, value moves from left to right: To the left of the company is cost; to the right is revenue. The two groups are attracted to each other—a phenomenon that economists call the network effect. Because of network effects, successful platforms enjoy increasing returns to scale. Fueled by the promise of increasing returns, competition in two-sided network industries can be fierce. Challenge 1

BMII: Brain Machine Interfacing Initiative CellarTracker - Wine Reviews & Cellar Management Tools Towards Reproducible Descriptions of Neuronal Network Models Introduction Science advances human knowledge through learned discourse based on mutual criticism of ideas and observations. This discourse depends on the unambiguous specification of hypotheses and experimental procedures—otherwise any criticism could be diverted easily. Moreover, communication among scientists will be effective only if a publication evokes in a reader the same ideas as the author had in mind upon writing [1]. Scientific disciplines have over time developed a range of abstract notations, specific terminologies and common practices for describing methods and results. Matrix notation provides an illustrative example of the power of notation. (1)Defining and , etc., we can write this more compactly as (2)Introducing matrix notation simplifies this further to (3)with multiple advantages: the equation is much more compact, since the summing operation is hidden, as well as the system size; most importantly, the equation is essentially reduced to a simple multiplication. , and .