2 dépôts
Awesome GitHub RepositoriesWeight Initialization Methods
Techniques for randomly initializing weight matrices and bias vectors in neural network layers based on layer dimensions.
Distinct from Layer Parameter Optimization: Distinct from Layer Parameter Optimization: focuses on initialization strategies rather than reducing parameter count or computational overhead.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Weight Initialization Methods. Refine with filters or upvote what's useful.
Awesome Weight Initialization Methods GitHub Repositories
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- rushter/mlalgorithms
rushter/MLAlgorithms
10,983Voir sur GitHubMLAlgorithms is an educational machine learning algorithm library consisting of core predictive models implemented from scratch in Python. It serves as a reference for developers to study the internal logic and mathematical workings of these models through clean, minimal implementations. The codebase focuses on the study of algorithm implementation and machine learning education, providing a way to understand internal mechanics by building components without relying on heavy external libraries. The project utilizes object-oriented encapsulation and NumPy-based vectorization to manage model s
Provides modular weight initialization strategies separated from the training loop to allow for various randomization techniques.
Python - kulbear/deep-learning-coursera
Kulbear/deep-learning-coursera
7,729Voir sur GitHubThis repository contains programming assignments and lecture notes from Andrew Ng's foundational deep learning course specialization on Coursera. The materials cover core neural network training techniques including optimization algorithms, normalization methods, regularization approaches, parameter initialization strategies, and learning rate scheduling to improve model convergence and generalization. The coursework explores design principles where successive neural network layers learn progressively more abstract feature representations from input data. It provides guidance on selecting ope
Randomly initialize weight matrices and bias vectors for each layer based on layer dimensions.
Jupyter Notebookcourseradeep-learning
