3 repositorios
Awesome GitHub RepositoriesGeneralization Techniques
Methods used to improve a model's ability to generalize to unseen data, such as dropout and data augmentation.
Distinct from Model Performance Optimization: Distinct from Model Performance Optimization: focuses on accuracy and generalization rather than hardware acceleration or quantization.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Generalization Techniques. Refine with filters or upvote what's useful.
Awesome Generalization Techniques GitHub Repositories
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- morvanzhou/tutorials
MorvanZhou/tutorials
12,952Ver en GitHubThis repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad
Applies dropout and batch normalization techniques to prevent overfitting and improve model generalization.
Pythonmachine-learningmultiprocessingneural-network - codebasics/py
codebasics/py
7,262Ver en GitHubThis project is a Python data science curriculum and programming tutorial collection. It provides a structured set of educational notebooks and scripts designed to teach data analysis, machine learning, and deep learning. The repository serves as a learning path for building and tuning predictive models, including regression, decision trees, and neural networks. It includes a data visualization guide for creating financial time-series plots and a multiprocessing reference for implementing parallel task execution and shared memory synchronization. The curriculum covers broader capability area
Teaches how to use dropout and data augmentation to improve model accuracy and generalization.
Jupyter Notebookjupyterjupyter-notebookjupyter-notebooks - ashishpatel26/andrew-ng-notes
ashishpatel26/Andrew-NG-Notes
3,594Ver en GitHubThis project is a collection of structured study notes and notebooks serving as an educational resource for deep learning and neural network fundamentals. It provides a technical reference for implementing machine learning theory, covering everything from basic network design to the construction of advanced architectures. The material specifically focuses on the implementation of convolutional neural networks for computer vision and sequence models for natural language processing. It includes detailed guidance on building object detection systems, face recognition, and speech transcription mo
Covers regularization techniques like dropout and L1/L2 norms to improve model generalization.
Jupyter Notebookandrew-ngandrew-ng-courseandrew-ng-machine-learning
