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deepAI

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

daily_stock_analysis

Language-model stock analysis system combining market data, news, dashboards, and automated notifications.

Popularity
Repository stars21167,453Highest
7-day net stars——
Fork8855,857Highest
30-day trendInsufficient historyInsufficient history
Maintenance
Last push2019-08-072026-11-02
Maintenance notesNot verifiedUnknown
Repository created2017-05-242026-01-10
License & use
LicenseGPL-3.0 · Share derivative sourceMIT · Permissive
PricingPricing not verifiedPricing not verified
Basics
Browse by purposeBusiness systemsAI & agents · Business systems
TypeApps & systemsApps & systems
SubcategoryBusiness managementBusiness management
PlatformNot verifiedNot verified
Cost detailsCheck current official software, hosting, model and infrastructure pricing.Verify software, hosting, model and infrastructure costs against the current official documentation.
Pricing source——
First collected2026-11-032026-11-02
MetricRepository stars, not unique users or individual-skill installsRepository Stars, not unique users
LanguageJupyter NotebookPython
About this projectDetection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.Language-model stock analysis system combining market data, news, dashboards, and automated notifications.
Measured at2026-11-02 19:01 UTC2026-11-02 19:26 UTC

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