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High performance library for time series distances (DTW) and time series clustering.
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FeatherCNN is a high performance inference engine for convolutional neural networks.
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An enterprise-grade, high performance feature store.
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Library for high performance deep learning inference on NVIDIA GPUs.
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A high-performance CLI tool to concurrently download shared files and entire folder structures from Google Drive, featuring multi-progress UI and robust conflict resolution.
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Autograd and XLA for high-performance machine learning research.
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An MCP tool that connects Google Ads with Claude AI/Cursor and others, allowing you to analyze your advertising data through natural language conversations. This integration gives you access to campaign information, performance metrics, keyword analytics, and ad management—all through simple chat with Claude, Cursor or Windsurf.
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A high performance, memory efficient, maximally parallelized ensemble learning, integrated with scikit-learn.
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Algorithm capable of fully capturing the impact of data drift on performance.
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Ncnn is a high-performance neural network inference framework optimized for the mobile platform.
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An Easy-to-Use and High-Performance AI deployment framework.
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An open-source cross-platform performance library for deep learning applications.
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OneFlow is a performance-centered and open-source deep learning framework.
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Distributed, masterless, high performance, fault tolerant data processing. Written entirely in Clojure.
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openserp tracks rankings, SERP visibility, and keyword performance across search results.
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Simple, realtime visualization of neural network training performance.
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Windows optimization guide and tool collection for performance, cleanup, and system tuning.
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The lightweight PyTorch wrapper for high-performance AI research.
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Qards is a blogging platform focused on performance and on closing the gap between content publishers and developers: https://qards.io
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📸 A powerful, high-performance React Native Camera library.
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100% free, open-source AI code review tool for analyzing git branch differences with comprehensive security, performance, and quality analysis.
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High-performance, high-precision CPU, GPU, and memory profiler for Python.
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High performance distributed data processing in NodeJS.
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Flexible, high-performance serving system for machine learning models.