Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Schedule: Deep learning sessions

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9:00am - 5:00pm Monday, September 25 & Tuesday, September 26
Machine Learning & Data Science
Location: 1A 01/02
SOLD OUT
Dana Mastropole (The Data Incubator)
Average rating: **...
(2.50, 2 ratings)
Dana Mastropole and Michael Li demonstrate TensorFlow's capabilities through its Python interface and explore TFLearn, a high-level deep learning library built on TensorFlow. Join in to learn how to use TFLearn and TensorFlow to build machine learning models on real-world data. Read more.
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9:00am12:30pm Tuesday, September 26, 2017
Machine Learning & Data Science, Spark & beyond
Location: 1A 12/14 Level: Intermediate
Vartika Singh (Cloudera), Jeffrey Shmain (Cloudera)
Average rating: **...
(2.50, 6 ratings)
Vartika Singh and Jeffrey Shmain walk you through various approaches using the machine learning algorithms available in Spark ML to understand and decipher meaningful patterns in real-world data. Vartika and Jeff also demonstrate how to leverage open source deep learning frameworks to run classification problems on image and text datasets leveraging Spark. Read more.
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9:00am12:30pm Tuesday, September 26, 2017
Artificial Intelligence, Machine Learning & Data Science
Location: 1A 18 Level: Intermediate
Mo Patel (Teradata), Junxia Li (Think Big Analytics)
Junxia Li and Mo Patel demonstrate how to apply deep learning to improve consumer recommendations by training neural nets to learn categories of interest for recommendations using embeddings. You'll also learn how to achieve wide and deep learning with WALS matrix factorization—now used in production for the Google Play store. Read more.
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9:00am5:00pm Tuesday, September 26, 2017
Location: 1A 06/07
Ben Lorica (O'Reilly Media), Assaf Araki (Intel), Jacob Schreiber (University of Washington), Alex Ratner (Stanford University), Madeleine Udell (Cornell University), Yunsong Guo (Pinterest), Katherine Heller (Duke University), Alan Nichol (Rasa), Gerard de Melo (Rutgers University), Tamara Broderick (MIT), Inbal Tadeski (Anodot), Daniel Kang (Stanford University), Bichen Wu (UC Berkeley), Shaked Shammah (Hebrew University)
A full day of hardcore data science, exploring emerging topics and new areas of study made possible by vast troves of raw data and cutting-edge architectures for analyzing and exploring information. Along the way, leading data science practitioners teach new techniques and technologies to add to your data science toolbox. Read more.
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1:30pm5:00pm Tuesday, September 26, 2017
Data science & advanced analytics, Machine Learning & Data Science
Location: 1A 23/24 Level: Intermediate
David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed)
Natural language processing is a key component in many data science systems that must understand or reason about text. David Talby, Claudiu Branzan, and Alex Thomas lead a hands-on tutorial on scalable NLP using spaCy for building annotation pipelines, TensorFlow for training custom machine-learned annotators, and Spark ML and TensorFlow for using deep learning to build and apply word embeddings. Read more.
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1:30pm5:00pm Tuesday, September 26, 2017
Machine Learning & Data Science
Location: 1A 21/22 Level: Beginner
julia lintern (Metis)
Julia Lintern offers a deep dive into deep learning with Keras, beginning with basic neural nets and before exploring convolutional neural nets and recurrent neural nets. Along the way, Julia explains both the design theory behind and the Keras implementations of today's most widely used deep learning algorithms. Read more.
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1:30pm5:00pm Tuesday, September 26, 2017
Artificial Intelligence
Location: 1A 12/14 Level: Intermediate
Josh Patterson (Skymind), Vartika Singh (Cloudera), Dave Kale (Skymind), Tom Hanlon (Skymind)
Average rating: **...
(2.00, 1 rating)
Josh Patterson, Vartika Singh, David Kale, and Tom Hanlon walk you through interactively developing and training deep neural networks to analyze digital health data using the Cloudera Workbench and Deeplearning4j (DL4J). You'll learn how to use the Workbench to rapidly explore real-world clinical data, build data-preparation pipelines, and launch training of neural networks. Read more.
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11:20am12:00pm Wednesday, September 27, 2017
Machine Learning & Data Science
Location: 1A 12/14 Level: Intermediate
Mikio Braun (Zalando SE)
Average rating: ***..
(3.71, 7 ratings)
Deep learning has become the go-to solution for many application areas, such as image classification or speech processing, but does it work for all application areas? Mikio Braun offers background on deep learning and shares his practical experience working with these exciting technologies. Read more.
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1:15pm1:55pm Wednesday, September 27, 2017
Artificial Intelligence, Machine Learning & Data Science
Location: 1A 12/14 Level: Intermediate
Yuhao Yang (Intel), Zhichao Li (Intel)
Average rating: ****.
(4.00, 2 ratings)
Yuhao Yang and Zhichao Li discuss building end-to-end analytics and deep learning applications, such as speech recognition and object detection, on top of BigDL and Spark and explore recent developments in BigDL, including Python APIs, notebook and TensorBoard support, TensorFlow model R/W support, better recurrent and recursive net support, and 3D image convolutions. Read more.
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2:55pm3:35pm Wednesday, September 27, 2017
Data science & advanced analytics, Machine Learning & Data Science
Location: 1A 12/14 Level: Intermediate
Joshua Patterson (NVIDIA), Michael Balint (NVIDIA), Satish Varma Dandu (NVIDIA)
Average rating: ****.
(4.00, 1 rating)
How can deep learning be employed to create a system that monitors network traffic, operations data, and system logs to reliably flag risk and unearth potential threats? Satish Dandu, Joshua Patterson, and Michael Balint explain how to bootstrap a deep learning framework to detect risk and threats in operational production systems, using best-of-breed GPU-accelerated open source tools. Read more.
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5:25pm6:05pm Wednesday, September 27, 2017
Artificial Intelligence, Machine Learning & Data Science
Location: 1A 12/14 Level: Intermediate
Leo Dirac (Amazon Web Services)
Average rating: *****
(5.00, 5 ratings)
Leo Dirac demonstrates how to apply the latest deep learning techniques to semantically understand images. You'll learn what embeddings are, how to extract them from your images using deep convolutional neural networks (CNNs), and how they can be used to cluster and classify large datasets of images. Read more.
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2:05pm2:45pm Thursday, September 28, 2017
Bargava Subramanian (Independent), Harjinder Mistry (Red Hat)
Average rating: ***..
(3.00, 1 rating)
Bargava Subramanian and Harjinder Mistry explain how machine learning and deep learning techniques are helping Red Hat build smart developer tools to make software developers become more efficient. Read more.
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2:55pm3:35pm Thursday, September 28, 2017
Emerging Technologies, Machine Learning & Data Science
Location: 1A 08/10 Level: Intermediate
Mike Pittaro (Dell EMC)
The advances we see in machine learning would be impossible without hardware improvements, but building a high-performance hardware platform is tricky. It involves hardware choices, an understanding of software frameworks and algorithms, and how they interact. Mike Pittaro shares the secrets of matching the right hardware and tools to the right algorithms for optimal performance. Read more.
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2:55pm3:35pm Thursday, September 28, 2017
Data science & advanced analytics, Machine Learning & Data Science
Location: 1A 12/14 Level: Intermediate
Josh Patterson (Skymind), Kirit Basu (StreamSets )
Enterprises building data lakes often have to deal with very large volumes of image data that they have collected over the years. Josh Patterson and Kirit Basu explain how some of the most sophisticated big data deployments are using convolutional neural nets to automatically classify images and add rich context about the content of the image, in real time, while ingesting data at scale. Read more.
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4:35pm5:15pm Thursday, September 28, 2017
Big data and the Cloud, Machine Learning & Data Science
Location: 1A 12/14 Level: Intermediate
Jon Fuller (KNIME), Olivia Klose (Microsoft)
Average rating: ***..
(3.00, 1 rating)
Jon Fuller and Olivia Klose explain how KNIME, Apache Spark, and Microsoft Azure enable fast and cheap automated classification of malignant lymphoma type in digital pathology images. The trained model is deployed to end users as a web application using the KNIME WebPortal. Read more.