Topic Modeling. Topic Modeling Topic models provide a simple way to analyze large volumes of unlabeled text. A "topic" consists of a cluster of words that frequently occur together. Using contextual clues, topic models can connect words with similar meanings and distinguish between uses of words with multiple meanings. For a general introduction to topic modeling, see for example Probabilistic Topic Models by Steyvers and Griffiths (2007). For an example showing how to use the Java API to import data, train models, and infer topics for new documents, see the topic model developer's guide. The MALLET topic model package includes an extremely fast and highly scalable implementation of Gibbs sampling, efficient methods for document-topic hyperparameter optimization, and tools for inferring topics for new documents given trained models.
Importing Documents: Once MALLET has been downloaded and installed, the next step is to import text files into MALLET's internal format. Hyperparameter Optimization. George John, CEO, Co-founder - Rocket Fuel. Chief Executive Officer George John has built a successful career by consistently generating insights and profits for marketers by analyzing huge amounts of historical data to predict response rates and target messages—helping Kraft sell more Miracle Whip, Amazon.com sell more books, and Wells Fargo sell more financial services.
Prior to co-founding Rocket Fuel, George led teams building high-tech systems to optimize marketing at Yahoo! , salesforce.com, Epiphany, and IBM, in roles spanning engineering, marketing, sales, and executive management. As a senior director for behavioral targeting and personalization at Yahoo! , his teams delivered systems to optimize marketing spend and personalize content and ads. As employee number 13 at Epiphany, George created a suite of uniquely usable and scalable data-mining tools. Watch: George John at the 10th Annual International Digital-Life-Design Innovation Conference Watch: George John on Mad Money Watch: George John on CNBC Watch: Big Data Defined. Gregory Dunn | Physical Graffiti. Case Study: How Keyword Research Works in the Wild. PBS Nova - Fractals - Hunting the Hidden Dimension.avi. Apache Mahout: Scalable machine learning and data mining.
JAGS - Just Another Gibbs Sampler. Building a recommendation engine, foursquare style. Mar 22nd Last summer, foursquare’s employee count had grown a bit beyond our office capacity (as we surged towards 20 employees) and we had people sitting in whatever open space we could find. We were split between floors, parked on folding tables, and crammed into couches and loveseats.
In one of those seats, @anoopr was playing around with building a map showing interesting places, which we called “Explore.” In the ensuing eight months, we went through several iterations and evolutions to arrive at the recommendations engine we launched two weeks ago as part of the foursquare 3.0 update. This blog post describes the process we used to develop this system, and some interesting tidbits we found along the way. After that initial discussion, we quickly set up an API endpoint for Explore and started adding and tweaking features. With the results we were seeing, we could already sense that Explore was going to become something awesome. Our mobile web test client What’s next? P.S. So, you want to build a recommendation engine? At PredictiveIntent, we had a lot of enquiries from people at companies who were not sure whether to build their own recommendation engine, plug in a lightweight recommendations solution, or dedicate some time to implementing “personalisation” properly.
Our advice usually consists of three main points: Focus on your goals – will spending too much time building a recommendation engine take your development cycle off track? The importance of technology – thowing a few lines of Javascript code on a side and manually uploading datafeeds might be sufficient for the time being, but it will restrict you from innovating with recommendations?
Don’t underestimate performance – can you support a 99.95% uptime with multiple redundancy systems, 60 millisecond response times, peak loads of >100 transactions per second, and more? However, there are many different variations that fall into two main camps: Recommendations and Personalisation. Recommendations And for most, that’s all.
Personalisation. Startup Lab Workshop: Cohort Analysis. Storm, distributed and fault-tolerant realtime computation. Doubly linked list. A doubly-linked list whose nodes contain three fields: an integer value, the link to the next node, and the link to the previous node. The two node links allow traversal of the list in either direction. While adding or removing a node in a doubly-linked list requires changing more links than the same operations on a singly linked list, the operations are simpler and potentially more efficient (for nodes other than first nodes) because there is no need to keep track of the previous node during traversal or no need to traverse the list to find the previous node, so that its link can be modified.
Nomenclature and implementation[edit] Basic algorithms[edit] Open doubly-linked lists[edit] record DoublyLinkedNode { prev // A reference to the previous node next // A reference to the next node data // Data or a reference to data } record DoublyLinkedList { DoublyLinkedNode firstNode // points to first node of list DoublyLinkedNode lastNode // points to last node of list } Traversing the list[edit]
Joseph Weizenbaum. Joseph Weizenbaum (8 January 1923 – 5 March 2008) was a German and American computer scientist and a professor emeritus at MIT. The Weizenbaum Award is named after him. Life and career[edit] Born in Berlin, Germany to Jewish parents, he escaped Nazi Germany in January 1936, emigrating with his family to the United States. He started studying mathematics in 1941 at Wayne University, in Detroit, Michigan.
In 1942, he interrupted his studies to serve in the U.S. Army Air Corps as a meteorologist, having been turned down for cryptology work because of his "enemy alien" status. After the war, in 1946, he returned to Wayne, obtaining his B.S. in Mathematics in 1948, and his M.S. in 1950.[1][2] Around 1952, as a research assistant at Wayne, Weizenbaum worked on analog computers and helped create a digital computer. Weizenbaum was the creator of the SLIP programming language. In 1996, Weizenbaum moved to Berlin and lived in the vicinity of his childhood neighborhood.[5][2] Works[edit] See also[edit] Delab.csd.auth.gr/papers/AIAISYMEON.pdf.
Substance · Towards open digital publishing. Uhuru Software. Hack our apps | Meemoo project by Forrest Oliphant. Welcome to meemooVilson Vieira Paper GIFforresto GIF+HTML workshopBrasstown animators HTML5 videoForrest Oliphant particles → trailsForrest Oliphant (more) particles → tileForrest Oliphant Megacam gridForrest Oliphant GIFs from 1989Forrest Oliphant recursive spiralForrest Oliphant hackable clock 0.1Forrest Oliphant digital rainbow clockForrest Oliphant play beethovenVilson Vieira, g200kg 8 bit synthVilson Vieira, g200kg mr.doob harmonyVilson Vieira, mr.doob web tunnelVilson Vieira.
Art meets the open web. Announcing the Mozilla Eyebeam Open(Art) Fellows Stefan Hechenberger and Addie Wagenknecht, Toby Schachman and Forrest Oliphant Today, Mozilla and the Eyebeam Art + Technology Center are pleased to announce the recipients of the first-ever Open(Art) Fellowships. Together, these creative technologists will be exploring the frontier of art and the open web as part of our new Open(Art) program.
Pushing the boundaries of creative code Supported in part by an award from the National Endowment for the Arts, the Open(Art) initiative is all about supporting projects that facilitate artistic expression and learning on the open web, using code to enable cutting-edge art, media and hardware production. Over the next six months, the fellows will create open source tools and works that enable creative production and open participation. And the fellows are… The 2013 Open(Art) fellows are: Forrest Oliphant: Meemoo Meemoo brings the power of app development to everyone. Meemoo Toby Schachman: Pixel Shaders.
Lucene - Apache Lucene Core. Apache LuceneTM is a high-performance, full-featured text search engine library written entirely in Java. It is a technology suitable for nearly any application that requires full-text search, especially cross-platform. Apache Lucene is an open source project available for free download. Please use the links on the right to access Lucene. Lucene offers powerful features through a simple API: Scalable, High-Performance Indexing over 150GB/hour on modern hardwaresmall RAM requirements -- only 1MB heapincremental indexing as fast as batch indexingindex size roughly 20-30% the size of text indexed Powerful, Accurate and Efficient Search Algorithms Cross-Platform Solution Available as Open Source software under the Apache License which lets you use Lucene in both commercial and Open Source programs100%-pure JavaImplementations in other programming languages available that are index-compatible The Apache Software Foundation.
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Popcorn.js | The HTML5 Media Framework. Customer development 2012. API. The future is now: 10 startups leading the way in ‘big data’ How can big data and smart analytics tools ignite growth for your company? Find out at DataBeat, May 19-20 in San Francisco, from top data scientists, analysts, investors, and entrepreneurs. Register now and save $200! “Big data” isn’t just an enterprise trend. It’s a technological innovation that is already making a difference in your life.
Police are mixing crime data and sociological information to anticipate incidences of crime. A small cadre of scientists in Silicon Valley is harnessing genetics data to detect early signs of disease. “Big Data” is one of the main themes of CloudBeat 2012, VentureBeat’s upcoming conference highlighting real cases of revolutionary cloud adoption. We believe it’s time to cut through the hype and show you some cool companies that use big data to further research in the fields of healthcare, law, government, and education. We narrowed it down to some ground-breaking favorites who helped define the field. Data as a Service/Metamarkets.
API. An Entrepreneur's Guide to Relationship Management: Who To Keep in Touch With, and How Often. Editor’s Note: This blog post has been excerpted from an article that was originally published by Zvi Band on the Contactually blog. When running my previous company, staying in touch was crucial to my business. We were one of dozens, maybe hundreds, of web development firms in the DC region, but we kept really busy (sometimes overwhelmingly so) thanks to a continual flow of referrals from past clients and our network. Come to think of it, I never accepted a client that came via anonymous websites (read: my own website). So relationship management was a core aspect of my role as founder.
I’ve now transitioned to co-founder and CEO of Contactually, an early-stage, venture-backed startup. Relationship management is now a core part of my responsibilities, and it’s absolutely live-or-die around our users and partners – and especially fundraising. Partners: keep in touch every 30 – We talk about deals we have in the pipeline, API providers, business development relationships, resellers, etc. What If Your Smartphone Could Read Your Mind? Kimera Is Working On It. Today's smartphones are pretty darned smart. Yet we are only scratching the surface of what these devices might do. What if our smartphones were actually intelligent? Able to perceive our actions and intentions and act upon them on our behalf?
That's the goal of a startup called Kimera. Apple's Siri, the current standard bearer for smartphone AI, has nominal contextual awareness; it understands whether you are speaking to it or trying to determine your location, for instance. "We want to deploy a 100% decentralized AI layer on top of the existing Internet. The Kimera AI system attempts to model the world and derive useful intelligence that lets it adapt to the individual. To do this, Kimera has set up a system that taps the Internet guided by the phone's sensors. "[A DMe] can belong to a human, a business, a location, am object, anything. The DMe interacts with the world through what Kimera calls a Salience Engine. The next layer is known as DMe Smart Agents.
BOOM Goes The Dynamite! 500 Startups Explodes With 2 New Periodic Elements, 12 Sensational Startups, and 1 Awesome Accelerator Program. 500 Startups announces today they have discovered the newest periodic elements – Upandtotherightium (Ur) and Viralium (Vi). In addition to this amazing new discovery which is awaiting official recognition by the International Union of Pure and Applied Chemistry, they are also proud to announce they are in the early stages of developing 12 remarkable new startups in their just-launched 500 Startups Accelerator program at their headquarters in Mountain View. The buzz among the science community is the 500 Startups Accelerator will soon eclipse the CERN Large Hadron Collider in Geneva, Switzerland, which has long held the title of the world’s largest and highest-energy particle accelerator. Several Russian physicists have raised concerns this new 500 Startups Accelerator could be dangerous and has the potential for creating wormholes with unknown outcomes, possibly even the creation of a black hole.
Specs of the 500 Startups Accelerator are detailed below www.internmatch.com www.baydin.com.
Sigma.js. Apply to TechStars. How I Built a Viral Node.js App in Just One Weekend - BestVendor.com. Parsing. Data mining. Knowledge & deep Search engine. Processing. Social media. Berlin startup scene. HR. Open Source Capitalism. Accelerators, Contests & Programs for Startups. SnapEngage | Live Chat for your Website | Live Support | Live Help. Product dev. How to Make Wealth. May 2004 (This essay was originally published in Hackers & Painters.) If you wanted to get rich, how would you do it? I think your best bet would be to start or join a startup. That's been a reliable way to get rich for hundreds of years. The word "startup" dates from the 1960s, but what happens in one is very similar to the venture-backed trading voyages of the Middle Ages.
Startups usually involve technology, so much so that the phrase "high-tech startup" is almost redundant. Lots of people get rich knowing nothing more than that. The Proposition Economically, you can think of a startup as a way to compress your whole working life into a few years. Here is a brief sketch of the economic proposition. Like all back-of-the-envelope calculations, this one has a lot of wiggle room.
If $3 million a year seems high, remember that we're talking about the limit case: the case where you not only have zero leisure time but indeed work so hard that you endanger your health. Startups are not magic.
The 18 Mistakes That Kill Startups. October 2006 In the Q & A period after a recent talk, someone asked what made startups fail. After standing there gaping for a few seconds I realized this was kind of a trick question. It's equivalent to asking how to make a startup succeed—if you avoid every cause of failure, you succeed—and that's too big a question to answer on the fly. Afterwards I realized it could be helpful to look at the problem from this direction. In a sense there's just one mistake that kills startups: not making something users want. 1. Have you ever noticed how few successful startups were founded by just one person? What's wrong with having one founder? But even if the founder's friends were all wrong and the company is a good bet, he's still at a disadvantage. The last one might be the most important. 2. Startups prosper in some places and not others. Why is the falloff so sharp? 3. If you watch little kids playing sports, you notice that below a certain age they're afraid of the ball. 4. 5. 6. 7. 8. 9.
Neo4j. Branding. Sales and marketing. Vc and investment. Lean startup tools. Cust dev. Opp strategy.