Showing 4 open source projects for "material cut optimization"

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  • 1
    Coursera-ML-AndrewNg-Notes

    Coursera-ML-AndrewNg-Notes

    Personal notes from Wu Enda's machine learning course

    ...The project aims to help students understand the mathematical concepts, algorithms, and intuition behind fundamental machine learning techniques taught in the course. It organizes the material into clear written summaries that accompany each lecture topic, including supervised learning, regression methods, neural networks, and optimization algorithms. The repository often expands on the original lecture material by adding additional explanations, diagrams, and formulas that clarify the theoretical foundations of the algorithms. ...
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  • 2
    Three.js Object Sculptor

    Three.js Object Sculptor

    Codex plugin that turns attached object images into code-only

    Three.js Object Sculptor is a Codex plugin that converts a reference image into a procedural Three.js object written entirely in code. It first evaluates whether the image is suitable and produces an ObjectSculptSpec describing geometry, materials, lighting, hierarchy, pivots, and quality targets. Codex then follows staged passes from blockout and structural work through surface detail, interaction design, and optimization. Generated models include meaningful sockets and anchors for...
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  • 3
    Machine Learning Engineering Open Book

    Machine Learning Engineering Open Book

    Machine Learning Engineering Open Book

    ...It is heavily oriented toward practitioners who need hands-on solutions, including copy-paste commands, infrastructure comparisons, and performance tuning strategies. The material spans the full ML lifecycle, from hardware selection and distributed training to inference optimization and debugging. Rather than focusing purely on theory, the project emphasizes engineering tradeoffs and production realities that often determine success at scale. It is continuously updated as a knowledge dump, making it especially valuable for engineers operating complex AI systems in the wild.
    Downloads: 0 This Week
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  • 4
    Auto-PyTorch

    Auto-PyTorch

    Automatic architecture search and hyperparameter optimization

    While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, another trend in AutoML is to focus on neural architecture search. To bring the best of these two worlds together, we developed Auto-PyTorch, which jointly and robustly optimizes the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch is mainly developed to support tabular data (classification, regression) and time series...
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