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USA: Day 2 - Hands-On Labs

Introduction To Deep Learning For Time Series Data

Harini Eavani, Varun V. Kumar, & Ruby Pai Time series data is an essential part of analysis in finance, healthcare, weather, industrial IoT, wearables and many other domains. In this hands-on workshop, participants will learn how to build and train an nGraph model for predictive maintenance of aircraft engines. We will discuss the use of different topologies for time-series modeling and understand their tradeoffs.

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USA: Day 2 - Hands-On Labs

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2:16:07
Build a Battery Powered Deep Learning Inference Engine
Build a Battery Powered Deep Learning Inference Engine
1:03:56
Build Deep Learning Powered Big Data Solutions with BigDL for Apache Spark*
Build Deep Learning Powered Big Data Solutions with BigDL for Apache Spark*
48:50
Deep Learning with Satellite Imagery
Deep Learning with Satellite Imagery
54:37
Designing Far-Field Speech Processing Systems with Intel and Alexa Voice Service
Designing Far-Field Speech Processing Systems with Intel and Alexa Voice Service
57:26
Fast-Track Machine Learning Operationalization Through Streaming Integration
Fast-Track Machine Learning Operationalization Through Streaming Integration
43:30
HIGH throughput Single-shot multi-box object detection (SSD) on edge devices using AWS Greengrass Lambdas
HIGH throughput Single-shot multi-box object detection (SSD) on edge devices using AWS Greengrass Lambdas
1:23:54
Inference with Intel - Hands-On Workshop + Fireside Chat Including A Case Study with GE Healthcare
Inference with Intel - Hands-On Workshop + Fireside Chat Including A Case Study with GE Healthcare
44:54
Introduction To Deep Learning For Time Series Data
Introduction To Deep Learning For Time Series Data
50:05
Operationalize AI for Instant Business Impact with Hybrid Transactional and Analytical Processing
Operationalize AI for Instant Business Impact with Hybrid Transactional and Analytical Processing
40:17
Optimizing CNN Inference on CPUs
Optimizing CNN Inference on CPUs
49:13
Practical Deep Learning in Personalization- Developing Retail Customer Use Case
Practical Deep Learning in Personalization- Developing Retail Customer Use Case
52:00
Using Deep Learning for Entity Detection and Intent Extraction in Natural Language
Using Deep Learning for Entity Detection and Intent Extraction in Natural Language

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