Strata Data Conference - San Jose 2018: Data Science and Machine Learning Section
Strata Data Conference - San Jose 2018: Data Science and Machine Learning Section
.MP4, AVC, 5000 kbps, 1920x1080 | English, AAC, 128 kbps, 2 Ch | 12.3 hours | 26.5 GB
.MP4, AVC, 5000 kbps, 1920x1080 | English, AAC, 128 kbps, 2 Ch | 12.3 hours | 26.5 GB
Strata San Jose 2018 offered thousands of top data scientists, analysts, engineers, and executives from around North America and the world with an opportunity to examine and absorb the best technologies and practices related to data engineering, architecture, machine learning, and AI. This video compilation provides a complete recording of the conference's keynote speeches, tutorials, and sessions, including unfettered access to the exclusive Strata Business Summit ("the missing MBA for data-driven business") and its Executive Briefings on how to turn data and algorithms into business advantage.
Approaching the pricing problem at Lyft - Ashivni Shekhawat (Lyft) 00:41:27
Graph analysis of 200,000 tweets from Russian Twitter trolls - Ryan Boyd (Neo4j) 00:42:40
Small pieces, loosely joined: A skater's code - Rodney Mullen (Almost Skateboards) 00:35:09
Deep credit risk ranking with LSTM - Kyle Grove (Teradata) 00:37:59
Who are we? The largest-scale study of professional data scientists - Miryung Kim (UCLA), Muhammad Gulzar (UCLA) 00:38:58
The current state of TensorFlow and where it's headed in 2018 - Rajat Monga (Google) 00:34:59
Using deep learning to solve challenging problems - Jeff Dean (Google) 00:41:05
Detecting time series anomalies at Uber scale with recurrent neural networks - Andrea Pasqua (Uber), Anny Chen (Uber) 00:42:15
Data science at Slack - Josh Wills (Slack) 00:39:48
Using computer vision to combat stolen credit card fraud - Karthik Ramasamy (Uber), Lenny Evans (Uber) 00:40:10
Breaking up the block: Using heterogenous population modeling to drive growth - Daniel Lurie (Pinterest) 00:38:00
Machine learning applications for the industrial internet - Joseph Richards (GE Digital) 00:45:11
Explaining machine learning models - Evan Kriminger (ZestFinance) 00:40:01
sparklyr, implyr, and more: dplyr interfaces to large-scale data - Ian Cook (Cloudera) 00:39:49
Word embeddings under the hood: How neural networks learn from language - Patrick Harrison (S&P Global) 00:41:12
Cataloging the visible universe through Bayesian inference at petascale in Julia - Keno Fischer (Julia Computing) 00:36:32
Building career advisory tools for the tech sector using machine learning - Simon Hughes (Dice.com), Yuri Bykov (Dice.com) 00:40:11
Interpretable machine learning products - Mike Lee Williams (Cloudera Fast Forward Labs) 00:44:32
Being smarter than dinosaurs: How NASA uses deep learning for planetary defense - Siddha Ganju (Deep Vision) 00:21:53
Graph analysis of 200,000 tweets from Russian Twitter trolls - Ryan Boyd (Neo4j) 00:42:40
Small pieces, loosely joined: A skater's code - Rodney Mullen (Almost Skateboards) 00:35:09
Deep credit risk ranking with LSTM - Kyle Grove (Teradata) 00:37:59
Who are we? The largest-scale study of professional data scientists - Miryung Kim (UCLA), Muhammad Gulzar (UCLA) 00:38:58
The current state of TensorFlow and where it's headed in 2018 - Rajat Monga (Google) 00:34:59
Using deep learning to solve challenging problems - Jeff Dean (Google) 00:41:05
Detecting time series anomalies at Uber scale with recurrent neural networks - Andrea Pasqua (Uber), Anny Chen (Uber) 00:42:15
Data science at Slack - Josh Wills (Slack) 00:39:48
Using computer vision to combat stolen credit card fraud - Karthik Ramasamy (Uber), Lenny Evans (Uber) 00:40:10
Breaking up the block: Using heterogenous population modeling to drive growth - Daniel Lurie (Pinterest) 00:38:00
Machine learning applications for the industrial internet - Joseph Richards (GE Digital) 00:45:11
Explaining machine learning models - Evan Kriminger (ZestFinance) 00:40:01
sparklyr, implyr, and more: dplyr interfaces to large-scale data - Ian Cook (Cloudera) 00:39:49
Word embeddings under the hood: How neural networks learn from language - Patrick Harrison (S&P Global) 00:41:12
Cataloging the visible universe through Bayesian inference at petascale in Julia - Keno Fischer (Julia Computing) 00:36:32
Building career advisory tools for the tech sector using machine learning - Simon Hughes (Dice.com), Yuri Bykov (Dice.com) 00:40:11
Interpretable machine learning products - Mike Lee Williams (Cloudera Fast Forward Labs) 00:44:32
Being smarter than dinosaurs: How NASA uses deep learning for planetary defense - Siddha Ganju (Deep Vision) 00:21:53
Strata Data Conference - San Jose 2018: Data Science and Machine Learning Section
Strata Data Conference - San Jose 2018: Data Science and Machine Learning Section
