Moozonian

πŸ’» Developer Nexus: AI Developer Conference

GitHub

hibuz/dev-conf-replay

πŸ€ 졜근 κ΅­λ‚΄ IT μ„Έλ―Έλ‚˜ 및 κ°œλ°œμžπŸ’» 컨퍼런슀 μ˜μƒμ˜ λ‹€μ‹œ λ³΄κΈ°πŸ‘€ 링크λ₯Ό ν•œκ³³μ— μ •λ¦¬ν–ˆμŠ΅λ‹ˆλ‹€!

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GitHub

kwhinnery-openai/aidev-conf

Samples and demos for the AI Developer Conference

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GitHub

IBM/ddc-2021-development-to-production

IBM Digital Developer Conference - Data & AI 2021 course showcasing aspects of the AI and Machine Learning lifecycle going from development to production.

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GitHub

google-research-datasets/cats4ml-dataset

This dataset is a result of the CATS4ML (Crowdsourcing Adverse Test Sets for Machine Learning) Data Challenge - an adversarial test-set sampling images and labels from the Open Images Dataset for state-of-the-art image classification models. The challenge invited participants to sample this existing publicly available dataset for images that are incorrectly classified by image classification models. It was announced at the HCOMP 2020 conference and ran for three months (Jan-Apr 2021) aiming at submissions by researchers and developers worldwide. This challenge is a first proof-of-concept for the approach using an existing AI dataset, and shows immediate positive impact on improving evaluation datasets in AI research. In the following subsections we describe the main components of the challenge pipeline and data used.

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