Pre-Rendered Regularization Images fou use with fine-tuning, especially for the current implementation of "Dreambooth"

Applied Sparse regularization (L1), Weight decay regularization (L2), ElasticNet, GroupLasso and GroupSparseLasso to Neuronal Network.

A Julia package to perform Tikhonov regularization for small to moderate size problems.

Python source code for EMNLP 2020 Findings paper: "Domain Adversarial Fine-Tuning as an Effective Regularizer".

All my Machine Learning Projects from A to Z in (Python & R)

Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.

fdaPDE: Physics-Informed Spatial and Functional Data Analysis

System developed by team datamafia in WNUT 2020 Task 2: Identification of informative COVID-19 English Tweets

Code and Data sets for the EMNLP-2021-Findings Paper "ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection"

A vast assortment of class regularization images in sets of 1500

Regularized Levenberg-Marquardt algorithm for nonlinear regression on small size datasets

All about machine learning

Sparse Gaussian graphical models with Sorted L-One Penalized Estimation

Notebooks developed in Mathematica for my Ph.D. thesis and other resources

Supplementary code for the paper "Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks" - an accepted paper of LOD2019

Implementation of all basic algorithms needed in Deep Learning

This is Collection of Regularization Deep learning techniques with code and paper

Using encoder-decoder neural networks to learn representations of personal walking style, and generating person-specific gait for desired activities.

Notebooks of programming assignments of Improving Deep Neural Networks course of deeplearning.ai on coursera in August-2019