Sep 23–26, 2019

Schedule: Deep Learning sessions

9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1E 07
Dylan Bargteil (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. Dylan Bargteil explores 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.
9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1A 03
Bargava Subramanian (Binaize Labs), Amit Kapoor (narrativeVIZ)
Recommendation systems play a significant role—for users, a new world of options; for companies, it drives engagement and satisfaction. Amit Kapoor and Bargava Subramanian walk you through the different paradigms of recommendation systems and introduce you to deep learning-based approaches. You'll gain the practical hands-on knowledge to build, select, deploy, and maintain a recommendation system. Read more.
9:00am12:30pm Tuesday, September 24, 2019
Location: 1E 12/13
Bruno Goncalves (Data For Science, Inc)
You'll go hands-on to learn the theoretical foundations and principal ideas underlying deep learning and neural networks. Bruno Gonçalves provides the code structure of the implementations that closely resembles the way Keras is structured, so that by the end of the course, you'll be prepared to dive deeper into the deep learning applications of your choice. Read more.
1:30pm5:00pm Tuesday, September 24, 2019
Location: 1A 12/14
Garrett Hoffman (StockTwits)
Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include Word2Vec, recurrent neural networks (RNNs) and variants (long short-term memory [LSTM] and gated recurrent unit [GRU]), and convolutional neural networks. Read more.
1:15pm1:55pm Wednesday, September 25, 2019
Location: 1A 06/07
Every NLP-based document-processing solution depends on converting scanned documents and images to machine readable text using an OCR solution, limited by the quality of scanned images. Nagendra Shishodia, Chaithanya Manda, and Solmaz Torabi explore how GAN can bring significant efficiencies in any document-processing solution by enhancing resolution and denoising scanned images. Read more.
1:15pm1:55pm Wednesday, September 25, 2019
Location: 1A 12/14
Shioulin Sam (Cloudera Fast Forward Labs)
Supervised machine learning requires large labeled datasets—a prohibitive limitation in many real world applications. But this could be avoided if machines could earn with a few labeled examples. Shioulin Sam explores and demonstrates an algorithmic solution that relies on collaboration between human and machine to label smartly, and she outlines product possibilities. Read more.
2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 06/07
Keshav Peswani (Expedia Group), Ashish Aggarwal (Expedia Group)
Observability is the key in modern architecture to quickly detect and repair problems in microservices. Modern observability platforms have evolved beyond simple application logs and include distributed tracing systems like Zipkin and Haystack. Keshav Peswani and Ashish Aggarwal explore how combining them with real-time, intelligent alerting mechanisms helps in the automated detection of problems. Read more.
2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 06/07
Tony Xing (Microsoft), Congrui Huang (Microsoft), Qiyang Li (Microsoft), Wenyi Yang (Microsoft)
Anomaly detection may sound old fashioned, yet it's super important in many industry applications. Tony Xing, Congrui Huang, Qiyang Li, and Wenyi Yang detail a novel anomaly-detection algorithm based on spectral residual (SR) and convolutional neural network (CNN) and how this method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention. Read more.
4:35pm5:15pm Wednesday, September 25, 2019
Location: 1A 06/07
Anirudh Koul (Microsoft), Meher Kasam (Square)
Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. Anirudh Koul and Meher Kasam take you through how you can get deep neural nets to run efficiently on mobile devices. Read more.
5:25pm6:05pm Wednesday, September 25, 2019
Location: 1A 06/07
The common perception of deep learning is that it results in a fully self-contained model. However, in most cases, these models have similar requirements for data preprocessing as does more "traditional" machine learning. Despite this, there are few standard solutions for deploying end-to-end deep learning. Nick Pentreath explores how the ONNX format and ecosystem addresses this challenge. Read more.
1:15pm1:55pm Thursday, September 26, 2019
Location: 3B - Expo Hall
Victor Dibia (Cloudera Fast Forward Labs)
Recent advances in machine learning frameworks for the browser such as TensorFlow provides the opportunity to craft truly novel experiences within frontend applications. Victor Dibia explores the state of the art for machine learning in the browser using TensorFlow and outlines its use in the design of Handtrack.js—a library for prototyping real-time hand detection in the browser. Read more.
2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 06/07
Ryan Foltz (Exabeam)
Unmanaged and foreign devices in the corporate networks pose a security risk, and the first step toward reducing this risk is the ability to identify them. Ryan Foltz walks you through a comprehensive device management machine learning model based on deep learning that performs anomaly detection based on only device names to flag devices that do not follow naming structures. Read more.
3:45pm4:25pm Thursday, September 26, 2019
Location: 1A 06/07
Sajan Govindan (Intel)
Sajan Govindan outlines CERN’s research on deep learning in high energy physics experiments as an alternative to customized rule-based methods with an example of topology classification to improve real-time event selection at the Large Hadron Collider. CERN uses deep learning pipelines on Apache Spark using BigDL and Analytics Zoo open source software on Intel Xeon-based clusters. Read more.
4:35pm5:15pm Thursday, September 26, 2019
Location: 1A 06/07
Naoto Umemori (NTT DATA), Masaru Dobashi (NTT DATA)
Giant hogweed is a highly toxic plant. Naoto Umemori and Masaru Dobashi aim to automate the process of detecting the plant with technologies like drones and image recognition and detection using machine learning. You'll see how they designed the architecture, took advantage of big data and machine and deep learning technologies (e.g., Hadoop, Spark, and TensorFlow), and the lessons they learned. Read more.

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