Vincent Granville *** (DSC) - Dr. Vincent Granville is a visiory data scientist with 15 years of big data, predictive modeling, digital and business alytics experience. Vincent is widely recognized as the leading expert in scoring technology, fraud detection and web traffic optimization and growth. Over the last ten years, he has worked in real-time credit card fraud detection with Visa, advertising mix optimization with CNET, change point detection with Microsoft, online user experience with Wells Fargo, search intelligence with InfoSpace, automated bidding with eBay, click fraud detection with major search engines, ad networks and large advertising clients. Most recently, Vincent launched Data Science Central, the leading social network for big data, business alytics and data science practitioners. Vincent is a former post-doctorate of Cambridge University and the tiol Institute of Statistical Sciences.
Intel announces AI strategy to drive breakthrough performance, democratize access and maximize societal benefits. Intel introduces industry's most comprehensive data center compute portfolio for AI: the new Intel Nervana platform. Intel aims to deliver up to 100x reduction in the time to train a deep learning model over the next three years compared to GPU solutions. Intel reinforces commitment to an open AI ecosystem through an array of developer tools built for ease of use and cross-compatibility, laying the foundation for greater innovation. Intel announces AI strategy to drive breakthrough performance, democratize access and maximize societal benefits.
Earlier this year, we used DataRobot, a machine learning platform, to test a large number of preprocessing, imputation and classifier combinations to predict out-of-sample performance. In this blog post, I'll take some time to first explain the results from a unique data set assembled from strategies run on Quantopian. Quants run backtests to assess the merit of a strategy, academics publish papers showing phenomenal backtest results, and asset allocators at hedge funds take backtests into account when deciding where to deploy capital and who to hire. The lift-chart below shows that the regressor does a pretty good job at predicting OOS Sharpe ratio.
In this blog post, I'll take some time to first explain the results from a unique data set assembled from strategies run on Quantopian. Quants run backtests to assess the merit of a strategy, academics publish papers showing phenomenal backtest results, and asset allocators at hedge funds take backtests into account when deciding where to deploy capital and who to hire. The lift-chart below shows that the regressor does a pretty good job at predicting OOS Sharpe ratio. For more on how we approached comparing backtest and out-of-sample performance on cohort algorithms and how we found the best results were achieved with DataRobot, you can view the full report of our test here.
Marketing automation platforms save time, improve efficiency and increase productivity; but, they do not provide deep insight into the 2.5 quintillion bytes of data being created every day as people move from screen to screen consuming information and making buying decisions. In November 2013, IBM introduced the Watson Ecosystem Program, opening up Watson as a development platform and giving companies the ability to build applications powered by Watson's cognitive computing intelligence. Watson is a cognitive technology that processes information more like a human than a computer -- by understanding natural language, generating hypotheses based on evidence, and learning as it goes. Rather than simply automating manual tasks, artificial intelligence adds a cognitive layer that infinitely expands marketers' ability to process data, identify patterns, and build intelligent strategies and content faster, cheaper and more effectively than humans.
SigOpt offers Bayesian optimization as a service to minimize the amount of trial and error required to find good structural parameters for DNNs and CNNs. After hyperparameter optimization was completed for each method, we compared accuracy using a completely held out data set (SHVN test set, 26k images) using the best configuration found in the tuning phase. Using SigOpt to optimize deep learning architectures instead of a standard approach like random search can translate to real savings in the total cost of tuning a model. Development and innovation is often slowed by the complexity and effort required to find optimal structure and training strategies for deep learning architectures.
It include algorithms such as Linear Regression, Logistic Regression, Decision Tree, Random Forest etc. This includes Decision Trees, Random Forest. In this article, I've explained machine learning algorithms to a soldier in terms of war, battle, and strategy. Do you find watching battle, wars interesting?
Jeffrey S. Rosenschein and Vineet Singh Heuristic Programming Project Computer Science Department Stanford University Stanford, CA 94305 Abstract Meta-level control, in an Artificial Intelligence system, can provide increased capabilities This improvement, however, is achieved at the cost of the meta-level effort itself. This paper outlines a formalization of the costs involved in choosing between independent problem-solving methods: the cost of meta-level control is explicitly included. It is often desirable for Artificial Intelligence systems to make use of explicit knowledge about what they know; this tneta-level knowledge allows a program to direct its own activities in an informed and efficient manner [I] [21. The use of meta-level knowledge by a system to control its own actions is called'new-level confrol. If we are to gain efficiene; thi-migh the use of meta-level effort, \'.e must be sure that %'.hat is aved at the base level is not canceled by what is expended at the rnota-level.