4 projects for "network" with 2 filters applied:

  • Full-stack observability with actually useful AI | Grafana Cloud Icon
    Full-stack observability with actually useful AI | Grafana Cloud

    Our generous forever free tier includes the full platform, including the AI Assistant, for 3 users with 10k metrics, 50GB logs, and 50GB traces.

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  • 1
    deep-q-learning

    deep-q-learning

    Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

    ...Because it’s self-contained and Python-based, it's well-suited for experimentation, modifications, or extension — for instance adapting to custom Gym environments, tweaking network architecture, or combining with other RL techniques.
    Downloads: 0 This Week
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  • 2
    TensorFlow Course

    TensorFlow Course

    Simple and ready-to-use tutorials for TensorFlow

    This repository houses a highly popular (~16k stars) set of TensorFlow tutorials and example code aimed at beginners and intermediate users. It includes Jupyter notebooks and scripts that cover neural network fundamentals, model training, deployment, and more, with support for Google Colab.
    Downloads: 0 This Week
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  • 3
    Learn_Deep_Learning_in_6_Weeks

    Learn_Deep_Learning_in_6_Weeks

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

    Learn_Deep_Learning_in_6_Weeks compresses an introductory deep learning curriculum into six weeks of structured learning and practice. It begins with neural network fundamentals and moves through convolutional and recurrent architectures, optimization strategies, regularization, and transfer learning. The materials emphasize code-first understanding: building small models, training them on accessible datasets, and analyzing their behavior. Each week culminates in a tangible outcome—such as a working classifier or sequence model—so progress is visible and motivating. ...
    Downloads: 0 This Week
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  • 4
    LearningToCompare_FSL

    LearningToCompare_FSL

    Learning to Compare: Relation Network for Few-Shot Learning

    LearningToCompare_FSL is a PyTorch implementation of the “Learning to Compare: Relation Network for Few-Shot Learning” paper, focusing on the few-shot learning experiments described in that work. The core idea implemented here is the relation network, which learns to compare pairs of feature embeddings and output relation scores that indicate whether two images belong to the same class, enabling classification from only a handful of labeled examples.
    Downloads: 0 This Week
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  • Gemini 3 and 200+ AI Models on One Platform Icon
    Gemini 3 and 200+ AI Models on One Platform

    Access Google's best plus Claude, Llama, and Gemma. Fine-tune and deploy from one console.

    Build, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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