4 Repos
Awesome GitHub RepositoriesDeep Learning Code Libraries
Repositories containing executable scripts for deep learning workflows.
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Awesome Deep Learning Code Libraries GitHub Repositories
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- aymericdamien/tensorflow-examples
aymericdamien/TensorFlow-Examples
43,749Auf GitHub ansehenThis repository serves as a structured educational resource for machine learning and deep learning, providing a library of executable scripts and notebooks. It is designed to help users master the practical application of data processing, model evaluation, and neural network construction through annotated code samples and guided tutorials. The collection focuses on translating theoretical mathematical concepts into functional code, offering proven patterns for common tasks such as classification and regression. By providing curated examples of layer construction and training loops, the reposi
Provides a structured library of executable scripts and notebooks covering neural network architectures and optimization techniques.
Jupyter Notebookdeep-learningexamplesmachine-learning - chiphuyen/stanford-tensorflow-tutorials
chiphuyen/stanford-tensorflow-tutorials
10,377Auf GitHub ansehenThis project is a collection of deep learning tutorials and practical implementations using TensorFlow. It provides a neural network implementation guide through code examples designed for research-oriented deep learning. The repository covers supervised and unsupervised learning workflows, including the development of sequence models for language processing and chatbots. It includes specific examples for image style transfer and the use of autoencoders for feature extraction. The project also provides demonstrations for managing large-scale datasets using binary record formats and streaming
Provides a library of executable scripts for deep learning visualization and model monitoring.
Pythonchatbotcourse-materialsdeep-learning - mingchaozhu/deeplearning
MingchaoZhu/DeepLearning
7,679Auf GitHub ansehenThis project is a deep learning implementation library and neural network theory repository. It translates mathematical derivations from textbooks and literature into functional Python code to demonstrate how deep learning algorithms work. The codebase focuses on low-level algorithm implementation by using numerical libraries instead of high-level deep learning frameworks. This approach maps theoretical mathematical proofs to executable functions to verify principles and expose the underlying arithmetic and data flow of neural networks. The project covers the implementation of deep learning
Provides a collection of executable Python scripts that translate deep learning textbooks into functional code.
Pythonbayesiandeep-learningensemble-learning - cs230-stanford/cs230-code-examples
cs230-stanford/cs230-code-examples
4,218Auf GitHub ansehenThis repository provides structured code examples and project templates designed for classroom instruction in machine learning and neural networks. It offers reference implementations of deep learning models for both computer vision and natural language processing tasks, built using PyTorch as the core framework. The codebase is organized as a modular project template with separate directories for data handling, model definitions, and training scripts, promoting reusability and clarity. It includes predefined pipelines for image classification and text processing, along with a command-line in
Provides reference implementations of deep learning models for computer vision and natural language processing tasks.
Pythoncomputer-visionnatural-language-processingpytorch
