Presented By
O’Reilly + Cloudera
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
San Francisco, CA
Schedule: Deep Learning sessions
9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Robert Schroll (The Data Incubator)
Average rating:
(4.50, 2 ratings)
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.
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9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Francesca Lazzeri and Jen Ren walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources.
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9:00am - 5:00pm Monday, March 25 & Tuesday, March 26
Ian Cook (Cloudera)
Average rating:
(4.00, 1 rating)
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.
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9:00am–12:30pm Tuesday, March 26, 2019
Martin Gorner (Google)
Average rating:
(4.50, 4 ratings)
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.
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9:00am–12:30pm Tuesday, March 26, 2019
Average rating:
(4.75, 8 ratings)
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.
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1:30pm–5:00pm Tuesday, March 26, 2019
Average rating:
(4.17, 6 ratings)
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.
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1:30pm–5:00pm Tuesday, March 26, 2019
Average rating:
(3.00, 6 ratings)
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.
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11:00am–11:40am Wednesday, March 27, 2019
Jeremy Howard ( fast.ai | USF | doc.ai and platform.ai)
Average rating:
(4.80, 5 ratings)
Jeremy Howard describes how to leverage the latest research from the deep learning and HCI communities to train neural networks from scratch—without code or preexisting labels. He then shares case studies in fashion, retail and ecommerce, travel, and agriculture where these approaches have been used.
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11:50am–12:30pm Wednesday, March 27, 2019
Melinda Han Williams (Dstillery)
Average rating:
(4.86, 14 ratings)
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.
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11:50am–12:30pm Wednesday, March 27, 2019
Ron Bodkin (Google)
Average rating:
(4.33, 6 ratings)
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.
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2:40pm–3:20pm Wednesday, March 27, 2019
Chenhui Hu (Microsoft)
Average rating:
(4.67, 6 ratings)
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.
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2:40pm–3:20pm Wednesday, March 27, 2019
Sonal Gupta (Facebook)
Average rating:
(4.40, 5 ratings)
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.
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4:20pm–5:00pm Wednesday, March 27, 2019
Yogesh Pandit (Roche),
Saif Addin Ellafi (John Snow Labs),
Vishakha Sharma (Roche Molecular Solutions)
Average rating:
(4.67, 3 ratings)
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.
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4:20pm–5:00pm Wednesday, March 27, 2019
Luyang Wang (Restaurant Brands International),
Jing (Nicole) Kong (Office Depot),
Guoqiong Song (Intel),
Maneesha Bhalla (Office Depot)
Average rating:
(4.00, 2 ratings)
User-based real-time recommendation systems have become an important topic in ecommerce. Lu Wang, Nicole Kong, Guoqiong Song, and Maneesha Bhalla 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.
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4:20pm–5:00pm Wednesday, March 27, 2019
Gungor Polatkan (LinkedIn)
Average rating:
(4.33, 3 ratings)
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.
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5:10pm–5:50pm Wednesday, March 27, 2019
Zhenxiao Luo (Twitter)
Average rating:
(4.00, 4 ratings)
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. Zhenxiao Luo explains how Uber runs real-time analytics with deep learning.
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11:00am–11:40am Thursday, March 28, 2019
Fang Yu (DataVisor)
Average rating:
(3.75, 4 ratings)
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.
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11:50am–12:30pm Thursday, March 28, 2019
Average rating:
(4.75, 4 ratings)
Video is now the largest source of data on the internet, so we need tools to make it easier to process and analyze. Alex Poms and Will Crichton offer an overview of Scanner, the first open source distributed system for building large-scale video processing applications, and explore real-world use cases.
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11:50am–12:30pm Thursday, March 28, 2019
Average rating:
(2.67, 3 ratings)
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.
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11:50am–12:30pm Thursday, March 28, 2019
Sricharan Kumar (Intuit )
Average rating:
(4.29, 7 ratings)
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.
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1:50pm–2:30pm Thursday, March 28, 2019
Aashish Sheshadri (PayPal)
Average rating:
(4.50, 2 ratings)
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.
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1:50pm–2:30pm Thursday, March 28, 2019
Piero Molino (Uber AI)
Average rating:
(4.60, 5 ratings)
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.
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2:40pm–3:20pm Thursday, March 28, 2019
Average rating:
(2.86, 7 ratings)
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.
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3:50pm–4:30pm Thursday, March 28, 2019
Average rating:
(2.67, 3 ratings)
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.
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4:40pm–5:20pm Thursday, March 28, 2019
Christopher Lennan (idealo.de)
Average rating:
(4.00, 1 rating)
Idealo.de recently trained convolutional neural networks (CNN) for aesthetic and technical image quality predictions. Christopher Lennan shares the training approach, along with some practical insights, and sheds light on what the trained models actually learned by visualizing the convolutional filter weights and output nodes of the trained models.
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