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Fair Is Not the Default - Library - Google Design
When the child smiled or laughed, participants agreed universally that the infant’s dominant emotion was joy. But when the child interacted with something jarring, like a buzzer or a jack-in-the-box, there was a split. If a participant had been told the child was a girl, they thought her dominant emotion was fear. But if they'd been told the child was a boy, participants thought the dominant emotion was anger. Same child, same reaction, different perception.
machine_learning 
yesterday by amy
A Promenade of PyTorch – Peter Goldsborough
A brief discussion of a research-first deep learning framework
python  machine_learning  pytorch  howto  overview 
2 days ago by grinful
Getting the Look: Clothing Recognition and Segmentation for Automatic Product Suggestions in Everyday Photos
We present a scalable approach to automatically suggest rel- evant clothing products, given a single image without meta- data. We formulate the problem as cross-scenario retrieval: the query is a real-world image, while the products from online shopping catalogs are usually presented in a clean en- vironment. We divide our approach into two main stages: a) Starting from articulated pose estimation, we segment the person area and cluster promising image regions in order to detect the clothing classes present in the query image. b) We use image retrieval techniques to retrieve visually similar products from each of the detected classes. We achieve cloth- ing detection performance comparable to the state-of-the-art on a very recent annotated dataset, while being more than 50 times faster. Finally, we present a large scale clothing suggestion scenario, where the product database contains over one million products.
clothing  image_analysis  machine_learning  research_paper 
2 days ago by hydeph
design | Architecture and UX design of KAML-D
KAML-D can be deployed on any cloud (or on-premises) platform that allows you to run Kubernetes. Most of the components are open source. As a SaaS, it integrates with the cloud providers (user) identity management system, on-prem something like LDAP.

Existing open source components KAML-D uses:

Kubernetes for workload management and to ensure portability
TensorFlow for machine learning execution
JupyterHub for data scientists (dev/test of algorithms)
Storage layer: To hold the datasets, Minio, Ceph, as well as cloud-provider specific offerings such as EBS, with built-in dotmesh support for snapshots
New components KAML-D introduces:

KAML-D Workbench: a graphical UI for data scientists, data engineers, developers, and SREs to manage datasets as well as to test and deploy ML algorithms. Builds on the metadata layer to find and visualize datasets. Builds on the storage layer to store and load datasets.
KAML-D Metadata Hub: a data and metadata layer using PrestoDB and Elasticsearch for indexing and querying datasets.
KAML-D Observation Hub: a comprehensive observability suite for SREs and admins (as well as developers on the app level) to understand the health of the KAML-D platform and troubleshoot issues on the platform and application level:
Prometheus and Grafana for end-to-end metrics and monitoring/alerting
EFK stack for (aggregrated) logging
Jaeger for (distributed) tracing
The user management and access control part is outside of the scope of KAML-D but standard integration points such as LDAP are offered.
machine_learning  kubernetes  TensorFlow 
2 days ago by amy

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