5 repository-uri
Awesome GitHub RepositoriesDeep Learning Reference Implementations
Standardized scripts for training and running state-of-the-art deep learning models to ensure performance consistency.
Distinct from Deep Learning Framework Implementations: Unlike candidate implementations which focus on specific RL algorithms or frameworks, this covers general SOTA deep learning reference scripts.
Explore 5 awesome GitHub repositories matching artificial intelligence & ml · Deep Learning Reference Implementations. Refine with filters or upvote what's useful.
Awesome Deep Learning Reference Implementations GitHub Repositories
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- nvidia/deeplearningexamples
NVIDIA/DeepLearningExamples
14,819Vezi pe GitHubThis project is a collection of optimized scripts, deployment patterns, and reference implementations designed for scaling and accelerating state-of-the-art AI models. It serves as a multi-domain model zoo and a distributed training framework, providing PyTorch reference implementations for training and deploying models on GPU-accelerated infrastructure. The repository distinguishes itself through an optimization suite focused on NVIDIA GPU hardware, utilizing automatic mixed precision and specialized math modes to increase training speed and throughput. It provides enterprise deployment patt
Ships reference scripts for training state-of-the-art deep learning models with consistent accuracy across environments.
Jupyter Notebookcomputer-visiondeep-learningdrug-discovery - dragen1860/tensorflow-2.x-tutorials
dragen1860/TensorFlow-2.x-Tutorials
6,351Vezi pe GitHubThis project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a deep learning implementation guide for constructing diverse neural network architectures, including convolutional, recurrent, and generative networks. The repository provides templates and examples for several specialized domains, including computer vision for image classification and object detection, natural language processing for text generation and language understanding, and generative AI for synthesizing data using adversarial networks and autoencoders. It also includes
Provides standardized reference implementations for building and running diverse deep learning models.
Jupyter Notebookartificial-intelligencecomputer-visiondeep-learning - fastai/courses
fastai/courses
5,742Vezi pe GitHubThis project is a comprehensive set of educational resources and structured curricula for learning artificial intelligence and deep learning. It provides a machine learning curriculum consisting of lecture materials and interactive notebooks centered on implementing models using the PyTorch framework. The instructional design follows a code-first approach, where students implement working models before studying the underlying theoretical mathematics. The curriculum is delivered via executable documents that combine live code, equations, and narrative text to guide the implementation and deplo
Offers structured lessons and notebooks as a framework for building and deploying advanced deep learning models.
Jupyter Notebook - deep-learning-with-pytorch/dlwpt-code
deep-learning-with-pytorch/dlwpt-code
5,224Vezi pe GitHubAcest proiect este o resursă educațională de deep learning constând în implementări de modele PyTorch și exemple de cod. Oferă scripturi Python funcționale și notebook-uri pentru construirea, antrenarea și optimizarea rețelelor neuronale folosind calculul bazat pe tensori. Repository-ul include implementări pentru proiectarea straturilor de rețea personalizate și a funcțiilor de pierdere (loss functions), precum și exemple de fluxuri de lucru de transfer learning care încarcă ponderi de model pre-antrenate pentru a accelera dezvoltarea. Codul sursă acoperă o gamă largă de capabilități de deep learning, inclusiv antrenarea rețelelor neuronale, proiectarea componentelor de model personalizate și implementarea arhitecturilor multi-strat pentru a recunoaște modele complexe în seturi de date.
Translates theoretical deep learning concepts into functional reference implementations in Python.
Jupyter Notebookdeep-learningdeep-neural-networkspython - jaungiers/lstm-neural-network-for-time-series-prediction
jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction
5,206Vezi pe GitHubAcest proiect este un model de prognoză a seriilor temporale implementat în Python și Keras. Este un sistem de deep learning conceput pentru a prezice valori viitoare în seturi de date secvențiale prin antrenarea rețelelor neuronale de tip long short-term memory (LSTM) pe date numerice istorice. Implementarea se concentrează pe analiza datelor secvențiale, aplicând în mod specific aceste modele la predicția pieței financiare pentru a prognoza mișcările prețurilor și tendințele. Arhitectura acoperă preprocesarea datelor prin scalarea caracteristicilor min-max și transformări de tip sliding-window. Utilizează celule de rețea neuronală recurentă cu mecanisme de gating pentru dependențe pe termen lung și folosește backpropagation prin timp pentru calculul gradientului.
Provides a Python-based implementation for training and executing deep learning models.
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