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USA: Day 2 - Hands-On Labs

Inference with Intel - Hands-On Workshop + Fireside Chat Including A Case Study with GE Healthcare

Dave Chevalier, Principal Engineer, CT - Imaging, GE Healthcare Dmitry Rizshkov, Deep Learning Solution Architect Prashant Shah, Principal Engineer, Intel Optimizing training and decreasing time to train has a lot of industry attention. However, trained models need to be deployed to be used. This conversation will explore the path to optimized inference with GE as a specific example.

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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
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Build Deep Learning Powered Big Data Solutions with BigDL for Apache Spark*
Build Deep Learning Powered Big Data Solutions with BigDL for Apache Spark*
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Deep Learning with Satellite Imagery
Deep Learning with Satellite Imagery
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Designing Far-Field Speech Processing Systems with Intel and Alexa Voice Service
Designing Far-Field Speech Processing Systems with Intel and Alexa Voice Service
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Fast-Track Machine Learning Operationalization Through Streaming Integration
Fast-Track Machine Learning Operationalization Through Streaming Integration
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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
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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
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Optimizing CNN Inference on CPUs
Optimizing CNN Inference on CPUs
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Practical Deep Learning in Personalization- Developing Retail Customer Use Case
Practical Deep Learning in Personalization- Developing Retail Customer Use Case
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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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