5 Repos
Awesome GitHub RepositoriesMachine Learning Model Implementations
Practical code examples and structural implementations of various machine learning model types.
Distinct from Machine Learning Implementations: The candidates are either too focused on APIs, portability, or specific causal methods; this is about the general act of implementing models.
Explore 5 awesome GitHub repositories matching artificial intelligence & ml · Machine Learning Model Implementations. Refine with filters or upvote what's useful.
Awesome Machine Learning Model Implementations GitHub Repositories
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- snowkylin/tensorflow-handbook
snowkylin/tensorflow-handbook
3,927Auf GitHub ansehenDieses Projekt ist eine umfassende Bildungsressource und ein Tutorial-Handbuch für das Erstellen, Trainieren und Bereitstellen von Machine-Learning-Modellen mit TensorFlow 2. Es dient als strukturierter Lernleitfaden für grundlegende Deep-Learning-Konzepte, einschließlich neuronaler Netzwerkarchitekturen, automatischer Differenzierung und Tensor-Operationen. Das Handbuch bietet technische Anleitungen zur Optimierung der Ausführungseffizienz durch GPU-Speicherverwaltung, verteiltes Training und Modellquantisierung. Es enthält zudem detaillierte Anleitungen für den Aufbau leistungsfähiger Datenpipelines und den Export von Modellen für Produktionsserver, mobile Geräte und Webbrowser. Das Material deckt ein breites Spektrum an Funktionen ab, darunter die Modellentwicklung mit konvolutionellen und rekurrenten Netzwerken, die Implementierung benutzerdefinierter Verlustfunktionen und Layer sowie die Nutzung vortrainierter Modelle für Transfer Learning. Zudem werden Bereitstellungsstrategien für Edge-Geräte und die Nutzung cloudbasierter Runtimes zur Hardwarebeschleunigung behandelt. Die Ressource ist als Sammlung von Jupyter Notebooks implementiert.
Provides comprehensive guides and examples for implementing various machine learning model architectures.
Jupyter Notebook - dipanjans/practical-machine-learning-with-python
dipanjanS/practical-machine-learning-with-python
2,380Auf GitHub ansehenThis project serves as a comprehensive educational resource and curriculum for mastering machine learning and deep learning within the Python data science ecosystem. It provides a structured collection of tutorials and code examples designed to guide users through the end-to-end process of building, training, and deploying predictive models. The material focuses on practical implementation, covering the construction of machine learning pipelines that integrate data processing, feature engineering, and model training. It distinguishes itself by offering hands-on guidance for complex domains, i
Builds and executes predictive models through iterative training and validation methodologies.
Jupyter Notebookclassificationclusteringcomputer-vision - patchy631/machine-learning
patchy631/machine-learning
1,540Auf GitHub ansehenThis repository serves as an educational collection of interactive notebooks and code examples designed to demonstrate fundamental machine learning and deep learning concepts. It provides a structured environment for exploring data science workflows, ranging from basic numerical computing and statistical analysis to the construction of complex neural network architectures. The project distinguishes itself through a focus on hands-on experimentation, offering practical implementations for tasks such as computer vision, natural language processing, and statistical simulation. Users can engage w
Provides practical code examples and structural implementations of various machine learning model types.
Jupyter Notebook - towardsai/tutorials
towardsai/tutorials
1,023Auf GitHub ansehenThis project is an educational collection of tutorials and executable code notebooks focused on data science, machine learning, deep learning, and natural language processing concepts in Python. It provides instructional resources covering statistical analysis, linear algebra, artificial intelligence algorithms, and step-by-step guides for developers learning data science. The repository covers a broad spectrum of computational and statistical capabilities, including neural network construction, gradient-based optimization techniques, curve fitting, regression modeling, and collaborative filt
Modifies loss functions using vector norms to penalize large weights and prevent overfitting.
Jupyter Notebookcollaborative-filteringdata-sciencedeep-learning - jwarmenhoven/coursera-machine-learning
JWarmenhoven/Coursera-Machine-Learning
This repository serves as an educational collection of Python implementations for fundamental machine learning algorithms and statistical models. It provides a structured environment for learning core concepts through interactive computational documents that combine live code, narrative text, and data visualizations. The codebase focuses on predictive modeling development, offering instructional examples for building and evaluating regression, classification, and neural network models. It utilizes standardized data science library interfaces to demonstrate how to implement and execute these a
Offers practical code examples and structural implementations of various machine learning model types.
Jupyter Notebookandrew-ngcoursera-machine-learningpredictive-modeling
