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Join Vijay Srinivas Agneeswaran and Abhishek Kumar to discuss recommender systems—particularly deep learning-based recommender systems in Tensor Flow—or ask any other questions you have about deep learning.
When feeling low, he recharges his spirits by singing Russian music with Slavyanka, the Bay Area’s Slavic music chorus.
Clinical collaboration benefits from pooling data to train models from large datasets, but it's hampered by concerns about sharing data.
) team within the AI R Group at Microsoft, where he focuses on machine learning applications for text analytics and natural language processing.
Mohamed works with Microsoft product teams and external customers to deliver advanced technologies that extract useful and actionable insights from unstructured free text such as search queries, social network messages, product reviews, customer feedback.
Previously, he spent three years at Microsoft Research’s Advanced Technology Labs.
He holds a Ph D in machine learning from the University of Ulm in Germany.Mohamed Abdel Hady and Zoran Dzunic demonstrate how to build a domain-specific entity extraction system from unstructured text using deep learning.In the model, domain-specific word embedding vectors are trained on a Spark cluster using millions of Pub Med abstracts and then used as features to train a LSTM recurrent neural network for entity extraction.Ritesh Agrawal and Anirban Deb explain how Uber uses machine learning to identify and stop rogue queries, saving both computational power and money.TV Digital Media, where he works on the platform that supports ABC’s streaming applications.Come learn how to build and operationalize machine learning models using distributed functions and do scalable, end-to-end data science in R and Python on single machines, Spark clusters, and cloud-based infrastructure.Ritesh Agrawal leads the intelligent infrastructure systems team at Uber, which focuses on scaling data infrastructure for Uber’s growing business needs now and foreseeable in the future.Balasubramanian Narasimhan, John-Mark Agosta, and Philip Lavori outline a privacy-preserving alternative that creates statistical models equivalent to one from the entire dataset.R and Python top the list of languages used in data science and machine learning, and data scientists and engineers fluent in one of these languages are increasingly marketable.Anthony Accardo is director of applied R&D and advanced development for media networks production, distribution, marketing, analytics, and digital media at Disney.His areas of focus include data, metadata, machine learning and artificial intelligence, digital media products, UI and UX, animation technologies, video operations, content development, content research, data science, game engines, AR, and VR.