4 repository-uri
Awesome GitHub RepositoriesLayer Parameter Optimization
Techniques to reduce computational overhead and parameters within neural network layers.
Distinct from Deep Learning Optimization: Focuses on reducing layer-level parameter count and overhead rather than general computational graph optimization
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Layer Parameter Optimization. Refine with filters or upvote what's useful.
Awesome Layer Parameter Optimization GitHub Repositories
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- rushter/mlalgorithms
rushter/MLAlgorithms
10,983Vezi pe GitHubMLAlgorithms este o bibliotecă educațională de algoritmi de machine learning, constând în modele predictive de bază implementate de la zero în Python. Aceasta servește drept referință pentru dezvoltatori pentru a studia logica internă și funcționarea matematică a acestor modele prin implementări curate și minimale. Codul sursă se concentrează pe studiul implementării algoritmilor și educația în machine learning, oferind o modalitate de a înțelege mecanismele interne prin construirea componentelor fără a se baza pe biblioteci externe grele. Proiectul utilizează încapsularea orientată pe obiecte și vectorizarea bazată pe NumPy pentru a gestiona starea modelului și a efectua operații matematice. Arhitectura sa pune accent pe transparență prin utilizarea logicii pure Python pentru a implementa primitive de algebră liniară și inițializarea modulară a parametrilor.
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
7,729Vezi pe 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 - tensorflow/swift
tensorflow/swift
6,131Vezi pe GitHubSwift for TensorFlow is a custom toolchain that extends the Swift language with first-class automatic differentiation and differentiable types, enabling gradient-based computation directly within the compiler. It integrates the Swift compiler with TensorFlow runtime and XLA backends, allowing tensor operations to be compiled and executed on hardware-accelerated hardware for high-performance machine learning. The project distinguishes itself through compiler-integrated automatic differentiation that computes gradients of user-defined functions and types during compilation, eliminating the need
Traverses nested parameter structures to apply optimizers for complex model architectures.
Jupyter Notebook - ai-dawang/plugnplay-modules
ai-dawang/PlugNPlay-Modules
4,968Vezi pe GitHubPlugNPlay-Modules is a collection of reusable PyTorch computer vision modules and deep learning architectural components. It provides a library of standardized building blocks for constructing neural networks, focusing on attention mechanisms, signal processing layers, and feature fusion modules. The project is distinguished by its extensive variety of attention primitives, covering spatial, channel, and temporal weighting, as well as specialized variants like deformable, frequency-enhanced, and linear-complexity attention. It also implements advanced signal processing tools within the neural
Implements computational efficiency improvements through separable and partial convolutions and stochastic depth.
Python
