Hardcore Data Science California 2015 / O'Reilly Media, Inc..

Format:

Conference/Event

Video

Author/Creator:

O'Reilly Media, Inc., author.

Conference Name:

Strata Conference + Hadoop World Conference (2014 : San Jose).

Language:

English

Subjects (All):

Database management.

Machine learning.

Computer vision.

Natural language processing (Computer science).

Genre:

Electronic videos.

Physical Description:

1 online resource (1 video file, approximately 5 hr., 43 min.)

Edition:

1st edition

Place of Publication:

O'Reilly Media, Inc., 2015.

System Details:

video file

Summary:

Push the envelope of data science by exploring emerging topics such as data management, machine learning, natural language processing, crowdsourcing, and algorithm design with this O’Reilly video collection—taken from the Hardcore Data Science sessions at Strata + Hadoop World 2015 in San Jose, California. This video collection includes: Beyond DNNs towards New Architectures for Deep Learning, with Applications to Large Vocabulary Continuous Speech Recognition Tara Sainath, Researcher, Google DNNs were first explored for acoustic modeling, where numerous research labs demonstrated improvements in WER between 10-40% relative. This session provides an overview of the latest improvements in deep learning across various research labs since the initial inception. On the Computational and Statistical Interface and "Big Data" Michael Jordan, Professor, UC Berkeley How does statistical decision theory provide a mathematical point of departure for achieving such a blending? In this session, you’ll learn theoretical tradeoffs between statistical risk, amount of data, and “externalities” such as computation, communication, and privacy. Interpretable Machine Learning in Practice Maya Gupta, Research and Development Manager, Google What makes a large machine learning system more interpretable and robust in practice? This session discusses the importance of monotonicity, smoothness, semantically meaningful inputs and outputs, and designing algorithms that are easy to debug. Visual Understanding Beyond Naming Alyosha Efros, Associate Professor, UC Berkeley This session describes some of the efforts to bypass the “language bottleneck” and other information to help in visual understanding and visual data mining. Finding Repeated Structure in Time Series Data: Commercial and Scientific Opportunities Eamonn Keogh, Professor, University of California - Riverside In this session, Eamonn argues that, relative to other types of data (text, social networks, etc.), time series data is relatively underexploited, and that many opportunities are available for novel commercial applications and scientific discoveries. Tensor Methods for Large-scale Unsupervised Learning: Applications to Topic and Community Modeling Anima Anandkumar, Faculty member, UC Irvine Understand how to exploit tensor methods for learning. Tensors are higher order generalizations of matrices, and are useful for representing rich information structures. Tensor factorization involves finding a compact ...

Participant:

Presenter, Ben Lorica ... [and eleven others].

Notes:

Online resource; Title from title screen (viewed June 16, 2015)

Title taken from thumbnail image on resource description page (Safari, viewed July 1, 2015)

Selections from Strata + Hadoop World 2014 (that is, 2015).

OCLC:

913535043