4 مستودعات
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
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- rushter/mlalgorithms
rushter/MLAlgorithms
10,983عرض على GitHubMLAlgorithms هي مكتبة تعليمية لخوارزميات التعلم الآلي تتكون من نماذج تنبؤية أساسية تم تنفيذها من الصفر بلغة Python. تعمل كمرجع للمطورين لدراسة المنطق الداخلي والآليات الرياضية لهذه النماذج من خلال تنفيذات نظيفة وبسيطة. يركز الكود المصدري على دراسة تنفيذ الخوارزميات وتعليم التعلم الآلي، مما يوفر طريقة لفهم الآليات الداخلية من خلال بناء المكونات دون الاعتماد على مكتبات خارجية ثقيلة. يستخدم المشروع التغليف الموجه للكائنات (object-oriented) والمتجهات القائمة على NumPy لإدارة حالة النموذج وإجراء العمليات الرياضية. وتؤكد بنيته على الشفافية باستخدام منطق Python الصرف لتنفيذ أساسيات الجبر الخطي وتهيئة المعلمات النمطية.
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
7,729عرض على 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
6,131عرض على 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
4,968عرض على 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
