Track:

Deep Learning Applications & Practices

Location: Cyril Magnin I

Day of week: Wednesday

Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

Track Host:
Soups Ranjan
Director of Data Science @Coinbase

Soups Ranjan is the Director of Data Science at Coinbase, one the largest bitcoin exchanges in the world. He manages the Risk & Data Science team that is chartered with preventing avoidable losses to the company due to payment fraud or account takeovers. Soups has a PhD in ECE on network security from Rice University. He has previously led the development of Machine Learning pipelines to improve performance advertising at Yelp and Flurry. He is the founder of RiskSalon.org, a round-table forum for risk professionals in San Francisco to share ideas on stopping bad actors.

SHORT TALK (10 MIN)

9:00am - 9:10am

Deep Learning Short Talk

Jendrik Joerdening, Data Scientist @Aurubis
CASE STUDY TALK (50 MIN)

9:20am - 10:10am

How Instacart Applies Deep Learning

Instacart has revolutionized grocery shopping by bringing groceries to your door in a little as an hour. Behind the scenes, Instacart uses machine learning for everything from routing shoppers to ranking search results. In this talk, Jeremy will discuss how Instacart is using deep learning to not only predict the sequence that shoppers will pick items in stores, but also to predict customer behavior, optimize search & ads, and personalize other aspects of their product experience. Lastly, he'll provide an overview of their work with Lore, a framework Instacart developed to make it easier to put deep learning models into production and share best practices.

Jeremy Stanley, VP of data science @Instacart
SHORT TALK (10 MIN)

10:35am - 10:45am

Tensorflow Jumpstart

Magnus Hyttsten, TensorFlow Developer Advocate @Google
CASE STUDY TALK (50 MIN)

10:55am - 11:45am

TensorFlow & TPU

Presentation details will be available soon.

Magnus Hyttsten, TensorFlow Developer Advocate @Google
SHORT TALK (10 MIN)

12:45pm - 12:55pm

Deep Learning Short Talk

CASE STUDY TALK (50 MIN)

1:05pm - 1:55pm

Interpretability of Machine Learning Models

Interpretable models are easier to improve. Regulators and society can better trust them to be safe and nondiscriminatory. They can also offer insights that can be used to change real-world outcomes for the better. But because there is a central tension between accuracy and interpretability interpretability can be hard to ensure.

I'll explore both the product case for interpretability and the academic research that is starting to make the inner workings of black box models such as deep neural networks easier to understand. In particular, I'll look at the application of a new open source tool called LIME to customer churn, image classification and black box NLP models.

Mike Lee Williams, Research engineer @Cloudera Fast Forward Labs
SHORT TALK (10 MIN)

2:20pm - 2:30pm

Deep Learning Short Talk

CASE STUDY TALK (50 MIN)

2:40pm - 3:30pm

Measuring Business Impact of Machine Learning System

I will provide an overview of how to do metric driven ML system development, primarily to answer following questions:

  • How do you bootstrap machine learning system for new objectives?
  • How do you tie up the ML system performances with business goals?
  • Do precision & recall always work as primary metrics?
  • How do you measure effectiveness of entire ML system in the context of product success?

These questions will be answered in the context of fraud detection case study.

Jevin Bhorania, Cash Data Science Lead @Square

Tracks

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.