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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.

9:00am - 9:10am

PyTorch by Example

Jendrik Jördening, Data Scientist @Nooxit

10:35am - 10:45am

Tensorflow Jumpstart

Magnus Hyttsten, TensorFlow Developer Advocate @Google

10:55am - 11:45am

TensorFlow: Pushing the ML Boundaries

Magnus Hyttsten, TensorFlow Developer Advocate @Google

12:45pm - 12:55pm

Basics of Deep Learning: No Math Required

Roland Meertens, Machine Learning Engineer @Autonomous Intelligent Driving

1:05pm - 1:55pm

Interpretable Machine Learning Products

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

2:20pm - 2:30pm

Detecting Similar Id Documents Using Deep Learning

Burkay Gur, Risk Engineer @Coinbase

2:40pm - 3:30pm

Understanding ML/DL Models using Interactive Visualization Techniques

In this talk we will see how visualization techniques can help us better understand machine learning and deep learning models. We will look at a specific financial use-case and see how interactive visualizations can help us not only improve the the model but also fix data issues. We will then take a look at tools to build, train and diagnose deep learning models applied on financial data sets.

Chakri Cherukuri, Quantitative Researcher @Bloomberg

2019 Tracks