Open Data Science Conference - West 2019 Videos

Open Data Science Conference - West 2019 (San Francisco, USA)

Presented By ODSC West 2019
ODSC West 2019
ODSC West 2019
The Top Data Science and Artificial Intelligence Video Content

Royalties for these videos help fund the ODSC Grant Award for open-source data science projects.

The Open Data Science Conference has established itself as the leading conference in the field of applied data science. Each ODSC event offers a unique opportunity to learn directly from the core contributors, experts, academics, and renowned instructors helping shape the field of data science and artificial intelligence

Presentations cover not only data science modeling, but also the languages like R and Python, tools like TensorFlow and RAPIDs, and exploring new updates and methods for Deep Neural Networks, Adversarial Attacks, and more. Below are also talks related to implementation and data science strategy meant for the curious business decision-maker.

Our conferences are organized around focus areas to ensure our attendees are at the forefront of this fast-emerging field and current with the latest data science languages, tools, and models.

Contact us at info@odsc.com.

Presentation Curriculum

Machine Learning
1352:51
Keynote: Towards a Blend of Machine Learning & Microeconomics - Michael I. Jordan FREE PREVIEW
Advanced Methods for Explaining XGBoost Models - Brian Lucena, PhD
Building a Portfolio for Applied Data Science Roles - Ben Weber, PhD
Building an Industry classifier with the latest scraping, NLP and deployment tools - Ido Shlomo
Causal Inference for Data Science - Vinod Bakthavachalam
Composable Machine Learning - Eric Xing, PhD
EMI: Embed, Measure and Iterate - Mayank Kejriwal, PhD
Enterprise Grade Data Labeling - Design Your Ground Truth to Scale in Production - Jai Natarajan
Explainable Machine Learning - Eitan Anzenberg
Healthcare NLP with a Doctor's Bag of Notes - Andrew Long, PhD
How to Build a Recommendation Engine That Isn’t Movielens - Max Humber
Incorporating Intent Propensities in Personalized Next Best Action Recommendation - Kexin Xie
Learning From Limited Data - Shanghang Zhang, PhD
Looking from Above: Object Detection and Other Computer Vision Tasks on Satellite Imagery - Xiaoyong Zhu, Siyu Yang
Machine Learning (ML) on Devices: Beyond the Hype - Divya Jain
Machine Learning for User Conversion and Global Marketplace Optimization at Upwork (Part 1: Optimize User Level Growth) - Thanh Tran, PhD
Machine Learning Interpretability Toolkit - Mehrnoosh Sameki, PhD
Missing Data in Supervised Machine Learning - Andras Zsom, PhD
Optuna: A Define-by-Run Hyperparameter Optimization Framework - Crissman Loomis
Principled Methods for Analyzing Weight Matrices of Modern Production Quality Neural Networks - Michael Mahoney, PhD Charles Martin, PhD
Product Search in E-Commerce: What to Optimize? - Liang Wu, PhD
Real-ish Time Predictive Analytics with Spark Structured Streaming - Scott J Haines
Responsible AI Requires Context and Connections - Amy E. Hodler
The Expense of Poorly Labeled Data. What Causes ML Models to Break? - Nikhil Kumar
When Your Big Data Seems Too Small: Accurate Inferences Beyond the Empirical Distribution - Gregory Valiant, PhD
Deep Learning
979:03
10 Things You Didn't Know About TensorFlow in Production - Chris Fregly
AI and Security: Lessons, Challenges and Future Directions - Dawn Xiaodong Song, PhD
An Inconvenient Truth about Artificial Intelligence - Yaron Singer, PhD
Combining Word Embeddings with Knowledge Engineering - Sanjana Ramprasad
Combining Word Embeddings with Knowledge Engineering - Sanjana Ramprasad
Community-Specific AI: Building Solutions for Any Audience - Jonathan Purnell, PhD Yacov Salomon, PhD
Deciphering the Black Box: Latest Tools and Techniques for Interpretability - Rajiv Shah, PhD
Enabling Powerful NLP Pipelines with Transfer Learning - Lars Hulstaert
Lessons Learned Deploying a Deep Learning Visual Search Service at Scale - Scott Cronin, PhD
Named Entity Recognition At Scale With Deep Learning - Sijun He
Neural Networks from Scratch with Pytorch - Brad Heintz
Planetary Scale Location-based Insights - Gopal Erinjippurath
Practical Deep Learning for Images, Sensor and Text - Renee Qian
Project GaitNet: Ushering in The ImageNet Moment for Human Gait kinematics - Vinay Prabhu, PhD
Spark NLP for Healthcare: Lessons Learned Building Real-World Healthcare AI Systems - Veysel Kocaman, PhD
Tutorial on Deep Reinforcement Learning: Part 1 - Pieter Abbeel, PhD
Tutorial on Deep Reinforcement Learning: Part 2 - Pieter Abbeel, PhD
Tutorial on Deep Reinforcement Learning: Part 3 - Pieter Abbeel, PhD
Validate and Monitor Your AI and Machine Learning Models - Olivier Blais
World-scale Deep Learning for Automated Driving - Sudeep Pillai, PhD