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

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

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 Network…

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.

Code for reproducing Manifold Mixup results (ICML 2019)

Image-processing software for cryo-electron microscopy

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

Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296

机器学习-Coursera-吴恩达- python+Matlab代码实现

Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

a Ready-to-use PyTorch Extension of Unofficial CutMix Implementations with more improved performance.

Generalized Linear Models in Sklearn Style

This Repository contains Solutions to the Quizes & Lab Assignments of the Machine Learning Specialization (2022) from Deeplearning.AI on Coursera taught by Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig.

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"