You are viewing content from a past/completed QCon -

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

Financial Crime Risk @RevolutApp

Soups Ranjan heads Financial Crime Risk at Revolut, the fastest growing challenger bank in Europe. He leads the team in charge of preventing financial crime on Revolut’s platform using data science and machine learning. Soups has 14 years of experience applying machine learning to domains ranging from network security to advertising and cryptocurrencies. Prior to Revolut, Soups was the director of data science and risk at Coinbase, one of the largest cryptocurrency exchanges in the world. At Coinbase, Soups built many engineering teams from the ground up including data, risk and identity. Soups is the co-founder of, a roundtable forum for risk professionals in San Francisco and Seattle to share ideas on stopping financial crime. Soups holds a PhD in ECE focused on network security from Rice University. Soups currently lives in Berkeley with his family.

9:00am - 9:10am

PyTorch by Example

An introduction to PyTorch, a comparison to other frameworks and how to build neural networks with it.

Jendrik Jördening, Data Scientist @Nooxit

9:20am - 10:10am

Recent Deep Learning Advancements, Revolution Far from Over!

It started with Backpropagation, convolutional neural networks and RMSprop but these are only the first wave of innovations that got deep learning started. Some recent advancements include Population Based Training (PBT), Neuroevolution and ML Managed Services. We will discuss how deep learning research is becoming increasingly automated, more computationally efficient and serverless.

You will learn tips on how to automatically search the best deepnet topology for your task, scale up your machine learning experiments to run on thousands of nodes and to do hyperparameter optimization like pro!

Arshak Navruzyan, Founder @fellowshipai

10:35am - 10:45am

Tensorflow Jumpstart

Join this session to get started with TensorFlow in the most efficient way. We'll give an overview of the different products & APIs and the best practice.

Magnus Hyttsten, TensorFlow Developer Advocate @Google

10:55am - 11:45am

TensorFlow: Pushing the ML Boundaries

Google has made extraordinary advances in Machine Learning (ML) over the past few years. These breakthroughs requires enormous amounts of computation, both to train as well as run the underlying machine learning models. That's why we've built and deployed the Tensor Processing Unit (TPU), to allow us to support larger and larger amounts of ML workloads. In this talk, we'll look at the architecture of the TPU, how you write effective TensorFlow code to utilize its power, and how Google is using it to break new frontiers in Machine Learning.

Magnus Hyttsten, TensorFlow Developer Advocate @Google

12:45pm - 12:55pm

Basics of Deep Learning: No Math Required

Recently deep learning has shattered all records when it comes to machine learning. Unfortunately, many developers never harness the power of this machine learning technique. In this short talk you will gain a basic understanding of the two most simple types of layers: the dense, and convolutional layer. Take the first steps in your journey to 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

Identity verification is the process in which we digitally confirm the legitimacy of a real world document. This process is necessary for many businesses to meet their compliance requirements and mitigate their fraud risk. A common form of fraud is the duplication and alteration of stolen documents across multiple user accounts. In this talk, we will discuss how Coinbase solves the problem of detecting similar identity 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

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.