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Quadcopter Navigation in the Forest using Deep Neural Networks

In the video presentation below, a group of deep learning researchers study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; the team proposes a different approach based on a Deep Neural Network used as a supervised image classifier.

Top 5 Mistakes When Writing Spark Applications

In the presentation below from Spark Summit 2016, Mark Grover goes over the top 5 things that he’s seen in the field that prevent people from getting the most out of their Spark clusters. When some of these issues are addressed, it is not uncommon to see the same job running 10x or 100x faster with the same clusters, the same data, just a different approach.

An Introduction to NoSQL Databases

In the video presentation below, Bart Baesens, Professor of Big Data & Analytics, discusses a series of non-relational database management systems which focus specifically on being highly scalable in a distributed environment: NoSQL databases. The presentation is an extract from an upcoming book Principles of Database Management.

How Computers Learn

This Vienna Gödel Lecture provides a fascinating talk by Peter Norvig, Research Director at Google Inc. in the field of intelligent computers. Norvig talks about his long experience in AI and Machine Learning. The talk explains how computers learn from examples and what are the promises and limitations of these techniques.

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.

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.

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.