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Operationalizing Data Science

In the video presentation below, Joel Horwitz, Vice President, Partnerships, Digital Business Group for IBM Analytics, discusses what it means to “operationalize data science” – basically what it means to harden the ops behind running data science platforms.

Why Machine Learning Algorithms Fall Short

The video presentation below is “Why Machine Learning Algorithms Fall Short (And What You Can Do About It)” by Jean-François Puget, speaking at MLconf SF 2016. The talk examines how to overcome challenges in creating self-learning systems that perform better and are built to stand the test of time.

Research of 1,001 Data Scientist LinkedIn Profiles

Data science is a super-hot topic and the data scientist job is the sexiest job of the 21st century according to the Harvard Business Review. But how does one actually become a data scientist? 365 DataScience gathered data from 1,001 publicly listed LinkedIn profiles of data scientists and prepared a compelling report “Studying 1,001 Data Scientist LinkedIn Profiles.”

Deep Learning, Language and Cognition

In this public lecture at the Institute for Advanced Study, Christopher Manning, Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and Computer Science, discusses some of the results in deep learning with respect to computer vision, speech, and language.

Machines: How Do They Learn and Where Are They Headed?

In this public lecture held at the Institute for Advanced Study on October 27, 2017, Sanjeev Arora, Visiting Professor in the School of Mathematics, and Richard Zemel, Visitor in the School of Mathematics, gave brief talks about the field of machine learning and its major technical challenges. A panel discussion moderated by Robbert Dijkgraaf, Director and Leon Levy Professor, followed.

Nearly 40% of Data Professionals Spend Half of their Time Prepping Data Rather than Analyzing It

TMMData, creator of flexible data integration and preparation platform Foundation, partnered with the Digital Analytics Association to survey its community about data priorities and challenges. The survey revealed that data access, quality and integration present persistent, interrelated roadblocks to efficient and confident analysis across industries. Most notably, nearly 40% of data professionals (37.5%) spend more than 20 hours per week accessing, blending and preparing data rather than performing actual analysis.

Things You Can Do with a Recurrent Neural Network

In this video presentation from the 2015 in Auckland, New Zealand, Douglas Bagnall examines a particularly hot topic in deep learning, namely recurrent neural networks, and all the things you can do with them.

Large Scale Deep Learning with TensorFlow

In this video presentation from the Spark Summit 2016 conference in San Francisco, Google’s Jeff Dean examines large scale deep learning with the TensorFlow framework. Jeff joined Google in 1999 and is currently a Google Senior Fellow.

AI World – Industry’s Premier Event Focused on Enterprise AI – Boston, December 11-13

AI World will be held December 11-13, 2017 at the Boston Marriott Copley in Boston, MA. Now in its’ second year, AI World has become the industry’s largest event focused on the state of the practice of enterprise AI and machine learning. AI World’s mission is to enable enterprise business and technology executives to learn how to successfully harness intelligent technologies to build competitive advantage, drive new business opportunities and accelerate innovation efforts.

Visualizing and Understanding Deep Neural Networks

In the video presentation below, Matthew Zeiler, PhD, Founder and CEO of Clarifai Inc, speaks about large convolutional neural networks. These networks have recently demonstrated impressive object recognition performance making real world applications possible. However, there was no clear understanding of why they perform so well, or how they might be improved.