Showing 4 open source projects for "math notes"

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    Super comprehensive deep learning notes

    Super comprehensive deep learning notes

    Super Comprehensive Deep Learning Notes

    Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural...
    Downloads: 0 This Week
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  • 2
    LLM Datasets

    LLM Datasets

    Curated list of datasets and tools for post-training

    LLM Datasets curates and standardizes datasets commonly used to train and fine-tune large language models, reducing the overhead of hunting down sources and normalizing formats. The repository aims to make datasets easy to inspect and transform, with scripts for downloading, deduping, cleaning, and converting to formats like JSONL that slot into training pipelines. It highlights instruction-tuning and conversation-style corpora while also pointing to code, math, or domain-specific sets for...
    Downloads: 0 This Week
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  • 3
    LineSolv

    LineSolv

    LineSolv: A cross-platform desktop natural-language calculator

    LineSolv is a natural-language calculator that understands phrases like $20 in euro - 5% discount or what is the 5 plus pi times five. It combines a powerful Go-based arithmetic engine with a clean, distraction-free desktop UI. Built with Wails v2 (Go + WebView), Vite, TypeScript, and CSS custom properties for theming.
    Downloads: 27 This Week
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  • 4
    Deep-Learning-Interview-Book

    Deep-Learning-Interview-Book

    Interview guide for machine learning, mathematics, and deep learning

    Deep-Learning-Interview-Book collects structured notes, Q&A, and concept summaries tailored to deep-learning interviews, turning scattered study into a coherent playbook. It spans the core math (linear algebra, probability, optimization) and the practitioner topics candidates actually face, like CNNs, RNNs/Transformers, attention, regularization, and training tricks. Explanations emphasize intuition first, then key formulas and common pitfalls, so you can reason through unseen questions rather than memorize trivia. ...
    Downloads: 0 This Week
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