Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

Schedule: Deep Learning sessions

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9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Robert Schroll (The Data Incubator)
The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Robert Schroll offers an overview of TensorFlow's capabilities in Python, demonstrating how to build machine learning algorithms piece by piece and how to use TensorFlow's Keras API with several hands-on applications. Read more.
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9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Francesca Lazzeri (Microsoft), Jen Ren (Microsoft)
Francesca Lazzeri walks you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources. Read more.
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9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Ian Cook (Cloudera)
Advancing your career in data science requires learning new languages and frameworks—but learners face an overwhelming array of choices, each with different syntaxes, conventions, and terminology. Ian Cook simplifies the learning process by elucidating the abstractions common to these systems. Through hands-on exercises, you'll overcome obstacles to getting started using new tools. Read more.
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9:00am12:30pm Tuesday, March 26, 2019
Martin Gorner (Google)
Martin Gorner leads a hands-on introduction to recurrent neural networks and TensorFlow. Join in to discover what makes RNNs so powerful for time series analysis. Read more.
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9:00am12:30pm Tuesday, March 26, 2019
David Talby (Pacific AI), Alex Thomas (Indeed), Claudiu Branzan (Accenture AI)
David Talby, Alex Thomas, and Claudiu Branzan lead a hands-on introduction to scalable NLP using the highly performant, highly scalable open source Spark NLP library. You’ll spend about half your time coding as you work through four sections, each with an end-to-end working codebase that you can change and improve. Read more.
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1:30pm5:00pm Tuesday, March 26, 2019
Abhishek Kumar (Publicis Sapient), Pramod Singh (Publicis Sapient)
Abhishek Kumar and Pramod Singh walk you through deep learning-based recommender and personalization systems they've built for clients. Join in to learn how to use TensorFlow Serving and MLflow for end-to-end productionalization, including model serving, Dockerization, reproducibility, and experimentation, and Kubernetes for deployment and orchestration of ML-based microarchitectures. Read more.
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1:30pm5:00pm Tuesday, March 26, 2019
Jason Dai (Intel), Yuhao Yang (Intel), Jennie Wang (Intel), Guoqiong Song (Intel)
Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLSListings, the World Bank, Baosight, and Midea/KUKA. Read more.
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11:00am11:40am Wednesday, March 27, 2019
Jeremy Howard (platform.ai)
When deep learning is able to be easily applied by non-engineers (that possess extensive domain expertise), we can accelerate not only the pace of industry adoption but also the rate at which we uncover interesting and relevant research problems. Read more.
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11:50am12:30pm Wednesday, March 27, 2019
Melinda Han Williams (Dstillery)
Customer segmentation based on coarse survey data is a staple of traditional market research. Melinda Han Williams explains how Dstillery uses neural networks to model the digital pathways of 100M consumers and uses the resulting embedding space to cluster customer populations into fine-grained behavioral segments and inform smarter consumer insights—in the process, creating a map of the internet. Read more.
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11:50am12:30pm Wednesday, March 27, 2019
Ron Bodkin (Google)
Google uses deep learning extensively in new and existing products. Join Ron Bodkin to learn how Google has used deep learning for recommendations at YouTube, in the Play store, and for customers in Google Cloud. You'll explore the role of embeddings, recurrent networks, contextual variables, and wide and deep learning and discover how to do candidate generation and ranking with deep learning. Read more.
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2:40pm3:20pm Wednesday, March 27, 2019
Chenhui Hu (Microsoft)
Dilated neural networks are a class of recently developed neural networks that achieve promising results in time series forecasting. Chenhui Hu discusses representative network architectures of dilated neural networks and demonstrates their advantages in terms of training efficiency and forecast accuracy by applying them to solve sales forecasting and financial time series forecasting problems. Read more.
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2:40pm3:20pm Wednesday, March 27, 2019
Sonal Gupta (Facebook)
Sonal Gupta explores practical systems for building a conversational AI system for task-oriented queries and details a way to do more advanced compositional understanding, which can understand cross-domain queries, using hierarchical representations. Read more.
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4:20pm5:00pm Wednesday, March 27, 2019
Yogesh Pandit (Roche), Saif Addin Ellafi (John Snow Labs), Vishakha Sharma (Roche Molecular Solutions)
Yogesh Pandit, Saif Addin Ellafi, and Vishakha Sharma discuss how Roche applies Spark NLP for healthcare to extract clinical facts from pathology reports and radiology. They then detail the design of the deep learning pipelines used to simplify training, optimization, and inference of such domain-specific models at scale. Read more.
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4:20pm5:00pm Wednesday, March 27, 2019
Luyang Wang (Office Depot), Jing (Nicole) Kong (Office Depot), Guoqiong Song (Intel), Maneesha Bhalla (Office Depot)
User-based real-time recommendation systems have become an important topic in ecommerce. Jennie Wang, Lu Wang, and Nicole Kong demonstrate how to build deep learning algorithms using Analytics Zoo with BigDL on Apache Spark and create an end-to-end system to serve real-time product recommendations. Read more.
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4:20pm5:00pm Wednesday, March 27, 2019
Gungor Polatkan (LinkedIn)
Talent search systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter expressed in terms of a search query. Gungor Polatkan shares the results of the company's deployment of deep learning models on a real-world production system serving 500M+ users through LinkedIn Recruiter. Read more.
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5:10pm5:50pm Wednesday, March 27, 2019
Zhenxiao Luo (Uber)
From determining the most convenient rider pickup points to predicting the fastest routes, Uber uses data-driven analytics to create seamless trip experiences. Inside Uber, analysts are using deep learning and big data to train models, make predictions, and run analytics in real time. This talk will share Uber’s engineering effort about running real time Analytics with deep learning. Read more.
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11:00am11:40am Thursday, March 28, 2019
Fang Yu (DataVisor)
Online fraud flourishes as online services become ubiquitous in our daily life. Fang Yu explains how DataVisor leverages cutting-edge deep learning technologies to address the challenges in large-scale fraud detection. Read more.
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11:50am12:30pm Thursday, March 28, 2019
Alex Poms (Stanford University), Will Crichton (Stanford University)
Systems like Spark made it possible to process big numerical/textual data on hundreds of machines. Today, the majority of data in the world is video. Scanner is the first open-source distributed system for building large-scale video processing applications. Scanner is being used at Stanford for analyzing TBs of film with deep learning on GCP, and at Facebook for synthesizing VR video on AWS. Read more.
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11:50am12:30pm Thursday, March 28, 2019
Alkis Simitsis (Micro Focus), Shivnath Babu (Unravel Data Systems | Duke University)
Alkis Simitsis and Shivnath Babu share an automated technique for root cause analysis (RCA) for big data stack applications using deep learning techniques, using Spark and Impala. The concepts they discuss apply generally to the big data stack. Read more.
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11:50am12:30pm Thursday, March 28, 2019
Sricharan Kumar (Intuit )
Machine learning is delivering immense value across industries. However, in some instances, machine learning models can produce overconfident results—with the potential for catastrophic outcomes. Kumar Sricharan explains how to address this challenge through Bayesian machine learning and highlights real-world examples to illustrate its benefits. Read more.
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1:50pm2:30pm Thursday, March 28, 2019
Deep learning using sequence-to-sequence networks (Seq2Seq) has demonstrated unparalleled success in neural machine translation. A less explored but highly sought-after area of forecasting can leverage recent gains made in Seq2Seq networks. Aashish Sheshadri explains how PayPal has applied deep networks to monitoring and alerting intelligence. Read more.
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1:50pm2:30pm Thursday, March 28, 2019
Piero Molino (Uber AI)
Piero Molino offers an overview of Ludwig, a deep learning toolbox that allows you to train models and use them for prediction without the need to write code. It's unique in its ability to help make deep learning easier to understand for nonexperts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. Read more.
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2:40pm3:20pm Thursday, March 28, 2019
Sridhar Alla (Comcast), Syed Nasar (Cloudera)
Any business big or small depends on analytics, whether the goal is revenue generation, churn reduction, or sales and marketing. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. Sridhar Alla and Syed Nasar share techniques used to evaluate the the quality of data and the means to detect the anomalies in the data. Read more.
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3:50pm4:30pm Thursday, March 28, 2019
Yuhao Yang (Intel), Jennie Wang (Intel)
Yuhao Yang and Jennie Wang demonstrate how to run distributed TensorFlow on Apache Spark with the open source software package Analytics Zoo. Compared to other solutions, Analytics Zoo is built for production environments and encourages more industry users to run deep learning applications with the big data ecosystems. Read more.
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4:40pm5:20pm Thursday, March 28, 2019
Christopher Lennan (idealo.de)
At idealo.de we trained Convolutional Neural Networks (CNN) for aesthetic and technical image quality predictions. We will present our training approach, practical insights, and shed some light on what the trained models actually learned by visualising the convolutional filter weights and output nodes of our trained models. Read more.