Filtering by: Technical Track

Transfer Learning and Fine-tuning Deep Convolution Neural Network model for Fashion images — Anusua Trivedi (Microsoft)
Oct
11
1:30 PM13:30

Transfer Learning and Fine-tuning Deep Convolution Neural Network model for Fashion images — Anusua Trivedi (Microsoft)

In this talk, we propose prediction techniques using deep learning on fashion images. We show how to build a generic deep learning model, which could be used with a fashion image to predict the clothing type in that image and generate fashion image description/captions. We propose a method to apply a pre-trained deep convolution neural network (DCNN) on images to improve prediction accuracy. We use an ImageNet pre-trained DCNN and apply fine-tuning to transfer the learned features to the prediction.

Anusua Trivedi is a Data Scientist at Microsoft’s Advanced Data Science & Strategic Initiatives team. She works on developing advanced Predictive Analytics & Deep Learning models. Prior to joining Microsoft, Anusua was a data scientist at a Supercomputer Center - Texas Advanced Computing Center (TACC). Anusua is a frequent speaker at machine learning and big data conferences.

Twitter: @anurive - Linkedin - Website

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Development and cloud deployment of machine learning models for heartbeat classification on data from wearable devices — Ikaro Silva (MC10)
Oct
11
11:30 AM11:30

Development and cloud deployment of machine learning models for heartbeat classification on data from wearable devices — Ikaro Silva (MC10)

Electrical heart signals are one of the most recorded and stored physiological data in healthcare. With cardiovascular diseases being the single most common cause of death in the world, automatic analysis of cardiac signals under normal ambulatory conditions is expected to play a crucial role in assisting clinicians identify health issues. A critical step towards this goal is the automatic classification of heartbeats. The purpose of this work is to showcase the development and deployment of a cloud system for heartbeat classification collected from wearable devices.

Dr. Ikaro Silva is a Data Scientist at MC10 Inc and is responsible for developing algorithms that process the biological signals collected through MC10's unique wearable form factors. Dr. Silva is also a research scientist at MIT, where he is involved in augmenting PhysioNet's open source software and research.

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