5 projects for "compact" with 2 filters applied:

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  • 1
    Machine learning algorithms

    Machine learning algorithms

    Minimal and clean examples of machine learning algorithms

    ...The repository includes implementations of both supervised and unsupervised learning techniques, along with dimensionality reduction and clustering methods. Many of the algorithms are written in a simplified style that prioritizes clarity and educational value over production-level optimization. Because the code is compact and easy to follow, it is often used as a learning resource by developers who want to understand how machine learning algorithms are constructed.
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  • 2
    Instant Neural Graphics Primitives

    Instant Neural Graphics Primitives

    Instant neural graphics primitives: lightning fast NeRF and more

    ...The system implements several neural graphics primitives including neural radiance fields, signed distance functions, neural images, and neural volumes. These representations are trained using a compact neural network combined with a multiresolution hash encoding that dramatically accelerates both training and rendering processes. The framework is capable of reconstructing detailed 3D scenes from images and generating realistic views of those scenes in real time. Compared with earlier neural radiance field approaches, instant-ngp significantly reduces training time and computational requirements, enabling models to be trained within seconds or minutes on modern GPUs.
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  • 3
    picoGPT

    picoGPT

    An unnecessarily tiny implementation of GPT-2 in NumPy

    picoGPT is a minimal implementation of the GPT-2 language model designed to demonstrate how transformer-based language models work at a conceptual level. The repository focuses on educational clarity rather than production performance, implementing the core components of the GPT architecture in a concise and readable way. It allows users to understand how tokenization, transformer layers, attention mechanisms, and autoregressive text generation operate in modern large language models. The...
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  • 4
    Machine Learning Beginner

    Machine Learning Beginner

    Machine Learning Beginner Public Account Works

    ...Step-by-step examples help learners see how data preparation, model training, evaluation, and iteration fit together. Because the scope is intentionally beginner-friendly, it’s an approachable springboard to more advanced resources. Educators also reference it as a compact toolkit for workshops and short intro courses.
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  • Stop Cyber Threats with VM-Series Next-Gen Firewall on Azure Icon
    Stop Cyber Threats with VM-Series Next-Gen Firewall on Azure

    Native application identity and user-based security for your Azure cloud

    Gain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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  • 5
    Siamese and triplet learning

    Siamese and triplet learning

    Siamese and triplet networks with online triplet mining in PyTorch

    Siamese and triplet learning is a PyTorch implementation of Siamese and triplet neural network architectures designed for learning embedding representations in machine learning tasks. These types of networks learn to map images into a compact feature space where the distance between vectors reflects the similarity between inputs. Such embeddings are commonly used in applications like face recognition, image similarity search, and few-shot learning. The repository demonstrates how to train these models using contrastive loss and triplet loss functions, which encourage embeddings of similar samples to be close while pushing dissimilar samples farther apart. ...
    Downloads: 0 This Week
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