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USA: Day 2 - Hands-On Labs

Practical Deep Learning in Personalization- Developing Retail Customer Use Case

Marianno Phielipp, Senior Data Scientist, Intel This session will develop a Deep Learning recommender system based on Wide and Deep and Retail data from scratch using ngraph, open source software. Will also present different techniques to incorporate other information when available into the model. Finally will show results indicating how DL techniques are performing better than traditional ML techniques in this particular case.

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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
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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
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Introduction To Deep Learning For Time Series Data
Introduction To Deep Learning For Time Series Data
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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
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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