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58:48

Build a Better Monster: Morality, Machine Learning and Mass Surveillance

Posted by Maciej Ceglowski  on  Jul 23, 2017 Posted by Maciej Ceglowski  on  Jul 23, 2017

Maciej Ceglowski wonders what tech companies can do to reduce the amount of data collected, closing the path to mass surveillance and bringing some morality in using ML with this data.

22:14

Fast, Scalable, Reusable: A New Perspective on Production ML/AI Systems

Posted by Ekrem Aksoy  on  Jul 12, 2017 Posted by Ekrem Aksoy  on  Jul 12, 2017

Ekrem Aksoy discusses why production ML/AI systems should have a different perspective than the usual DevOps perspective which works on data immune systems.

38:41

Deep Learning for Image Understanding at Scale

Posted by Stacey Svetlichnaya  on  Jul 05, 2017 Posted by Stacey Svetlichnaya  on  Jul 05, 2017

Stacey Svetlichnaya discusses strategies and challenges building deep learning systems for object recognition at scale, using automatic labels in Flickr image search as a case study.

36:31

In Depth TensorFlow

Posted by Illia Polosukhin  on  Jun 30, 2017 Posted by Illia Polosukhin  on  Jun 30, 2017

Illia Polosukhin keynotes on TensorFlow, introducing it and presenting the components and concepts it is built upon.

25:52

Comparing Deep Learning Frameworks

Posted by Jeffrey Shomaker  on  May 28, 2017 1 Posted by Jeffrey Shomaker  on  May 28, 2017 1

Jeffrey Shomaker covers the different types of deep learning frameworks and then focuses on neural networks, including business uses and 4 of the main systems (eg. Tensor Flow) that are open sourced.

39:06

Deep Learning Applications in Business

Posted by Diego Klabjan  on  May 07, 2017 Posted by Diego Klabjan  on  May 07, 2017

Diego Klabjan discusses models, implementations, and challenges developing applications for trading, forecasting, and healthcare, detailing relevant models and issues adopting and deploying them.

21:07

Machine Learning at Scale

Posted by Aditya Kalro  on  Apr 18, 2017 Posted by Aditya Kalro  on  Apr 18, 2017

Aditya Kalro discusses using large-scale data for Machine Learning (ML) research and some of the tools Facebook uses to manage the entire process of training, testing, and deploying ML models.

46:36

Products and Prototypes with Keras

Posted by Micha Gorelick  on  Apr 08, 2017 Posted by Micha Gorelick  on  Apr 08, 2017

Micha Gorelick shows how to build a working product with Keras, a high-level deep learning framework, discussing design decisions, and demonstrating how to train and deploy a model.

40:00

Deep Learning at Scale

Posted by Scott Le Grand  on  Apr 05, 2017 Posted by Scott Le Grand  on  Apr 05, 2017

Scott Le Grand describes his work at NVidia, Amazon and Teza, including the DSSTNE distributed deep learning framework.

43:41

Building Robust Machine Learning Systems

Posted by Stephen Whitworth  on  Apr 05, 2017 Posted by Stephen Whitworth  on  Apr 05, 2017

Stephen Whitworth talks about his experience at Ravelin, and provides useful practices and tips to help ensure our machine learning systems are robust, well audited, avoid embarrassing predictions.

35:13

Using NLP, Machine Learning & Deep Learning Algorithms to Extract Meaning from Text

Posted by David Talby  on  Apr 02, 2017 Posted by David Talby  on  Apr 02, 2017

David Talby walks through building a natural language annotations pipeline with domain-specific annotators, and using deep learning to automatically expand and update taxonomies.

45:11

Policing the Stock Market with Machine Learning

Posted by Cliff Click  on  Mar 28, 2017 Posted by Cliff Click  on  Mar 28, 2017

Cliff Click talks about SCORE, a solution for doing Trade Surveillance using H2O, Machine Learning, and a whole lot of domain expertise and data munging.

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