The Scaled ML Conference 2018

The ML “Avengers” (Image Source : Twitter)

RL Systems at RISE Lab — Ion Stoica [Slides]

Matroid — Reza Zadeh [Slides]

A Pod, the basic unit of K8s, is a collection of one or more containers [Image Source : Twitter]
Cat out of the bag [Image Source : Twitter]

Programming the 2.0 stack — Andrej Karpathy

Datasets vs Algorithms (Image Source : Twitter)
Karpathy explaining some speed-limit corner samples encountered by Tesla autopilots on a regular basis (Image source : Twitter)

ML Problems in Biomedicine— Jennifer Chayes [Slides]

Machine Learning Problems in Biomedicine (Source : Twitter)

Systems and Machine Learning — Jeff Dean [Slides]

TPUs available on beta [Image Source : NIPS’17]
ML Arxiv papers are outpacing Moore’s Law [Image source : NIPS’17]

Anima Anandkumar — Role of Tensors in Large-Scale Training [Slides]

Anima explaining multi-dimensionsional Tensors [Image source : Twitter]

Meta Learning and Self Play — Ilya Sutskever [Slides]

RL as cherry on top of the learning cake (Image Source : A New Path to AI)

Large Scale Deep Learning with Keras — Francois Chollet [Slides]

Keras usage is most dominant in the industry (both large companies and startups) and in the overall data science community. [Image Source : Twitter]

Hardware for Deep Learning — Bill Dally [Slides]

Rise of GPU Computing [Image Source : Nvidia Developer]

Graphcore — Simon Knowles

ML at Facebook : An Infrastructure View — Yangqing Jia [Slides]

Infrastructure for the an army of models [Source : Twitter]




Machine Learning Engineer at Adobe

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Pramod Srinivasan

Pramod Srinivasan

Machine Learning Engineer at Adobe

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