Popular GitHub Repositories for Dropout Regularization

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

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Training neural models with structured signals.

Official implementation of "Segment Any Anomaly without Training via Hybrid Prompt Regularization (SAA+)".

Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.

Implicit Geometric Regularization for Learning Shapes

Simple Implementation of many GAN models with PyTorch.

Keras implementation of the paper "3D MRI brain tumor segmentation using autoencoder regularization" by Myronenko A. (https://arxiv.org/abs/1810.11654).

Deep learning library in plain Numpy.

Multi-hop knowledge graph reasoning learned via policy gradient with reward shaping and action dropout

[CVPR 2023] DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models

:microscope: Nano size Theano LSTM module

Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels

A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization, TPAMI 2020

Artificial Intelligence Learning Notes.

This repo contains the experimentation of the most important deep learning techniques related to Regularization, Optimization and HPO while using LeNet CNN architecture as the playground.

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

A tensorflow implementation of Knowledge-aware Graph Neural Networks with Label Smoothness regularization

[CVPR 2022] Learning Multiple Adverse Weather Removal via Two-stage Knowledge Learning and Multi-contrastive Regularization: Toward a Unified Model