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

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

Training neural models with structured signals.

Official Pytorch implementation of CutMix regularizer

Early stopping for PyTorch

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

Code for reproducing Manifold Mixup results (ICML 2019)

2.86% and 15.85% on CIFAR-10 and CIFAR-100

Simple Implementation of many GAN models with PyTorch.

Image-processing software for cryo-electron microscopy

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

Deep Learning Specialization courses by Andrew Ng, deeplearning.ai

Generalized Linear Models in Sklearn Style

机器学习-Coursera-吴恩达- python+Matlab代码实现

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

The tools and syntax you need to code neural networks from day one.

Efficient Algorithms for L0 Regularized Learning

Ordered Weighted L1 regularization for classification and regression in Python

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

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"

Deep Learning Specialization Course by Coursera. Neural Networks, Deep Learning, Hyper Tuning, Regularization, Optimization, Data Processing, Convolutional NN, Sequence Models are including this Course.

Implementation of key concepts of neuralnetwork via numpy

MATLAB package of iterative regularization methods and large-scale test problems. This software is described in the paper "IR Tools: A MATLAB Package of Iterative Regularization Methods and Large-Scale Test Problems" that will be published in Numerical Algorithms, 2018.

Software for learning sparse Bayesian networks

🎓 My lecture notes and assignment solutions for the Coursera machine learning class taught by Andrew Ng.

A C++ toolkit for Convex Optimization (Logistic Loss, SVM, SVR, Least Squares etc.), Convex Optimization algorithms (LBFGS, TRON, SGD, AdsGrad, CG, Nesterov etc.) and Classifiers/Regressors (Logistic Regression, SVMs, Least Squares Regression etc.)

A set of machine learning experiments in Clojure

An implementation of DropConnect Layer in Keras

Statistical Models with Regularization in Pure Julia

Now on CRAN, bigKRLS combines bigmemory & RcppArmadillo (C++) for speed into a new Kernel Regularized Least Squares algorithm. Slides: