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Neural Network Components · Awesome GitHub Repositories

10 repos

Awesome GitHub RepositoriesNeural Network Components

Modular building blocks and custom layer definitions used to construct and customize neural network architectures.

Explore 10 awesome GitHub repositories matching artificial intelligence & ml · Neural Network Components. Refine with filters or upvote what's useful.

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  • pytorch/pytorch

    pytorch/pytorch

    97,601GitHubView on GitHub↗

    PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic diffe

    Pythonautograddeep-learninggpu
  • rasbt/LLMs-from-scratch

    rasbt/LLMs-from-scratch

    85,529GitHubView on GitHub↗

    This repository serves as an educational framework for building large language models from the ground up. It provides a structured curriculum that guides learners through the end-to-end lifecycle of model development, including data processing, architecture design, and optimization. By focusing on low-level implementat

    Jupyter Notebookaiartificial-intelligencechatbot
  • tensorflow/models

    tensorflow/models

    77,684GitHubView on GitHub↗

    This repository serves as a centralized collection of state-of-the-art deep learning architectures and reference implementations designed for research and application development. It provides a comprehensive toolkit for computer vision and natural language processing, offering pre-built models and training pipelines fo

    Python
  • d2l-ai/d2l-zh

    d2l-ai/d2l-zh

    75,708GitHubView on GitHub↗

    This project is an open-source, interactive educational platform designed to teach deep learning through a comprehensive, code-first curriculum. It provides a structured learning path that covers foundational mathematics, modern neural network architectures, and practical optimization techniques, enabling practitioners

    Pythonbookchinesecomputer-vision
  • scikit-learn/scikit-learn

    scikit-learn/scikit-learn

    65,178GitHubView on GitHub↗

    Scikit-learn is a machine learning library for predictive data analysis that provides a collection of algorithms for supervised and unsupervised learning. It functions as a comprehensive toolkit for data preprocessing, dimensionality reduction, and model selection, allowing users to classify data objects, predict conti

    Pythondata-analysisdata-sciencemachine-learning
  • keras-team/keras

    keras-team/keras

    63,858GitHubView on GitHub↗

    Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a di

    Pythondata-sciencedeep-learningjax
  • ultralytics/yolov5

    ultralytics/yolov5

    56,830GitHubView on GitHub↗

    YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time object detection, image classification, and instance segmentation. It provides a unified toolkit that manages the entire lifecycle of a model, from initial dataset configuration and hyperparameter tuning

    Pythoncoremldeep-learningios
  • deepfakes/faceswap

    deepfakes/faceswap

    54,974GitHubView on GitHub↗

    Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users

    Pythondeep-face-swapdeep-learningdeep-neural-networks
  • karpathy/nanoGPT

    karpathy/nanoGPT

    53,461GitHubView on GitHub↗

    nanoGPT is a lightweight engine for training and fine-tuning transformer-based language models from scratch. It provides a minimalist codebase designed for educational exploration and rapid experimentation with neural network architectures, utilizing self-attention and feed-forward layers to process sequences and predi

    Python
  • ultralytics/ultralytics

    ultralytics/ultralytics

    53,426GitHubView on GitHub↗

    Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification

    Pythonclicomputer-visiondeep-learning

Explore sub-tags

  • Backpropagation ImplementationsManual implementations of gradient-based optimization logic.
  • Checkpointing SystemsMechanisms for serializing model states to disk for fault tolerance.
  • Custom Layer ImplementationsUser-defined neural network layers that extend base framework functionality.
  • Deep Learning ImplementationsCodebases focused on the manual implementation of neural network architectures from first principles.
  • Inference EnsemblesTechniques that combine multiple model predictions to enhance accuracy during inference.
  • Loss Functions1 sub-tagMathematical functions used to measure the difference between predicted and actual outputs during neural network training.
  • Multilayer PerceptronsFeedforward neural networks consisting of multiple layers of neurons with non-linear activation functions.
  • Neural Network LayersModular components that perform specific mathematical transformations on input data within a neural network.
  • Neural Network Research ToolsMinimalist implementations of neural architectures intended for educational study and rapid prototyping.
  • Pipeline PatternsUnified interfaces that chain data transformation and model estimation steps into sequential, reproducible workflows.