valinet /
ExplorerPatcher
This project aims to enhance the working environment on Windows
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omarurbano / repository
The project aims to develop a Virtual Teaching Assistant (VTA) for NVIDIA Deep Learning Institute on the basis of their Jupyter Notebooks and Workshop Manuals.
The Virtual Teaching Assistant (VTA) is a Multimodal AI-Driven Learning Assistant designed for the NVIDIA Deep Learning Institute (DLI) at Washington State University. The system provides grounded, transparent assistance for learners working with instructional notebooks, diagrams, and recorded content.
Client: Parteek Kumar (NVIDIA DLI University Ambassador)
Completed Deliverables:
Technical Accomplishments:
VirtualTeachingAssistant_T18/
├── Sprints/ # Sprint documentation
├── Reports/ # Project reports
├── Code/ # Source code
├── Data/ # Datasets
├── Deployment/ # Deployment configs
├── Testing/ # Test strategies
├── Evaluation/ # Metrics and results
├── Reflections/ # Team reflections
├── Resources/ # Links and references
└── Rubric/ # Grading rubrics
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valinet /
This project aims to enhance the working environment on Windows
PKU-YuanGroup /
This project aim to reproduce Sora (Open AI T2V model), we wish the open source community contribute to this project.
ziahamza /
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microsoft /
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TheRealJoelmatic /
The intrusive "Ad blocker are not allowed on YouTube" message is annoying. This open-source project aims to address this issue by providing a solution to bypass YouTube's ad blocker detection
py-why /
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.