3 projects for "common" with 2 filters applied:

  • Go From AI Idea to AI App Fast Icon
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    Deep-Learning-Interview-Book

    Deep-Learning-Interview-Book

    Interview guide for machine learning, mathematics, and deep learning

    ...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. Many entries connect theory to implementation details, including how choices in activation, initialization, or normalization affect convergence and stability. The content is organized for fast review before an interview loop but is also deep enough for systematic study over weeks. ...
    Downloads: 0 This Week
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  • 2
    Learn_Deep_Learning_in_6_Weeks

    Learn_Deep_Learning_in_6_Weeks

    This is the Curriculum for "Learn Deep Learning in 6 Weeks"

    ...Each week culminates in a tangible outcome—such as a working classifier or sequence model—so progress is visible and motivating. The plan also introduces practical considerations like GPU usage, checkpoints, and debugging training dynamics. It aims to give you enough breadth to recognize common patterns and enough depth to implement them on your own problems.
    Downloads: 0 This Week
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  • 3
    LearningToCompare_FSL

    LearningToCompare_FSL

    Learning to Compare: Relation Network for Few-Shot Learning

    ...The repository provides training and evaluation code for standard few-shot benchmarks such as miniImageNet and Omniglot, making it possible to reproduce the experimental results reported in the paper. It includes model definitions, data loading logic, episodic training loops, and scripts that implement the N-way K-shot evaluation protocol common in few-shot research. Researchers can use this codebase as a starting point to test new ideas, modify relation modules, or transfer the approach to new datasets.
    Downloads: 0 This Week
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