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[AI/ML] Spatial Transformer Networks (STN) — Overview, Challenges And Proposed Improvements

Towards AI

Parallel combinations are effective when there are more than one parts to focus on in images (It was shown that of 2 STNs used on the CUB-200–2011 bird classification dataset, one became head-detector and the other became body-detector) However, STNs are notoriously known to […]

ML 105
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Reinventing a cloud-native federated learning architecture on AWS

AWS Machine Learning Blog

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. It is challenging to centralize such data for ML due to privacy requirements, high cost of data transfer, or operational complexity. The ML framework used at FL clients is TensorFlow.

AWS 118
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Amazon EC2 P5e instances are generally available

AWS Machine Learning Blog

Additionally, network latency can become an issue for ML workloads on distributed systems, because data needs to be transferred between multiple machines. DLAMI provides ML practitioners and researchers with the infrastructure and tools to quickly build scalable, secure, distributed ML applications in preconfigured environments.

AWS 109
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Improving air quality with generative AI

AWS Machine Learning Blog

The attempt is disadvantaged by the current focus on data cleaning, diverting valuable skills away from building ML models for sensor calibration. Qiong (Jo) Zhang , PhD, is a Senior Partner Solutions Architect at AWS, specializing in AI/ML. She holds 30+ patents and has co-authored 100+ journal/conference papers.

AWS 130
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Streamlining ETL data processing at Talent.com with Amazon SageMaker

AWS Machine Learning Blog

Established in 2011, Talent.com aggregates paid job listings from their clients and public job listings, and has created a unified, easily searchable platform. This can significantly shorten the time needed to deploy the Machine Learning (ML) pipeline to production. And, it does not require the code to be ported into PySpark.

ETL 115
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OCP Summit 2024: The open future of networking hardware for AI

Hacker News

Since helping found OCP in 2011, we’ve shared our data center and component designs, and open-sourced our network orchestration software to spark new ideas both in our own data centers and across the industry.

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Predicting new and existing product sales in semiconductors using Amazon Forecast

AWS Machine Learning Blog

& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We trained three models using data from 2011–2018 and predicted the sales values until 2021.