4 个仓库
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
10,983在 GitHub 上查看MLAlgorithms 是一个教育性机器学习算法库，由从零开始用 Python 实现的核心预测模型组成。它作为开发者的参考资料，通过简洁、最小化的实现来学习这些模型的内部逻辑和数学原理。 代码库专注于算法实现的研究和机器学习教育，提供了一种在不依赖繁重外部库的情况下，通过构建组件来理解内部机制的方法。 该项目利用面向对象封装和基于 NumPy 的向量化来管理模型状态并执行数学运算。其架构通过使用纯 Python 逻辑来实现线性代数原语和模块化参数初始化，从而强调透明度。
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
7,729在 GitHub 上查看This 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
6,131在 GitHub 上查看Swift 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
4,968在 GitHub 上查看PlugNPlay-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
