Python for《Deep Learning》,该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现

regularization 开源项目推荐(970 个)
regularization 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
热门项目 前 20
按 Star 数排序展示前 20 个 · GitHub 共 970 个仓库Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
Early stopping for PyTorch
Official Pytorch implementation of CutMix regularizer
Training neural models with structured signals.
Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.
Image-processing software for cryo-electron microscopy
Code for reproducing Manifold Mixup results (ICML 2019)
Simple Implementation of many GAN models with PyTorch.
[CVPR 2023] DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models
2.86% and 15.85% on CIFAR-10 and CIFAR-100
Deep Learning Specialization courses by Andrew Ng, deeplearning.ai
机器学习-Coursera-吴恩达- python+Matlab代码实现
本库包含由Andrew Ng、Eddy Shyu、Aarti Bagul、Geoff Ladwig在Coursera上 Deeplearning.AI 授课的机器学习专业(2022年)测验和实验作业的解答。
Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296
该项目在 GitHub 上没有填写描述
a Ready-to-use PyTorch Extension of Unofficial CutMix Implementations with more improved performance.
Generalized Linear Models
Codes and Datasets for paper RecSys'20 "SSE-PT: Sequential Recommendation Via Personalized Transformer" and NurIPS'19 "Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers"
Efficient Algorithms for L0 Regularized Learning
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 20 个项目;原始页面见 https://github.com/topics/regularization。
- 该话题在 GitHub 上共有 970 个仓库, 本站收录的是其中最热门的 20 个,不是全量列表。
- Star / Fork 每日自动刷新一次,实时数据以 GitHub 为准。
- 当前 20 个项目累计 2 万 Star,覆盖 6 种主要语言,数据更新于 2026-11-09。
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