timlautk

adawhatever

MATLAB implementation of AdaGrad, Adam, Adamax, Adadelta etc.

bayesian-stat-machine-learning

"Bayesian Statistics & Machine Learning" Reading Group at Northwestern Statistics

BCD-for-DNNs

Training Deep Neural Networks using Block Coordinate Descent Algorithms

BCD-for-DNNs-PyTorch

Code for Global Convergence of Block Coordinate Descent in Deep Learning (ICML 2019)

BCoAPG-plus

MATLAB Code for the paper "Accelerated Block Coordinate Proximal Gradients with Applications in High Dimensional Statistics"

BigDataAlgorithms

bregman_prox_langevin_mc

Code for Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopes (ICML 2022)

d2l-pytorch

This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.

deep-learning-notes

Experiments with Deep Learning

Deep-Reinforcement-Learning-Algorithms-with-PyTorch

timlautk/BCD-for-DNNs-PyTorch

Code for Global Convergence of Block Coordinate Descent in Deep Learning (ICML 2019)

timlautk/bayesian-stat-machine-learning

"Bayesian Statistics & Machine Learning" Reading Group at Northwestern Statistics

timlautk/BCD-for-DNNs

Training Deep Neural Networks using Block Coordinate Descent Algorithms

timlautk/BCoAPG-plus

MATLAB Code for the paper "Accelerated Block Coordinate Proximal Gradients with Applications in High Dimensional Statistics"

timlautk/bregman_prox_langevin_mc

Code for Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopes (ICML 2022)

timlautk/d2l-pytorch

This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.

timlautk/deep-learning-notes

Experiments with Deep Learning

timlautk/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

timlautk/DeepRL

Modularized Implementation of Deep RL Algorithms in PyTorch

timlautk/Duke-Tsinghua-MLSS-2017

Duke-Tsinghua Machine Learning Summer School 2017

timlautk/FAR-HO

Gradient based hyperparameter optimization & meta-learning package for TensorFlow

timlautk/higher

higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.

timlautk/Keras-GAN

Keras implementations of Generative Adversarial Networks.

timlautk/lmc-atomi

Code for Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms

timlautk/MAML-Pytorch

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

timlautk/MAML-TensorFlow

Faster and elegant TensorFlow Implementation of paper: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

timlautk/modded-nanogpt

NanoGPT (124M) in 5 minutes

timlautk/models

Models and examples built with TensorFlow

timlautk/normalizing-flows-tutorial

Tutorial on normalizing flows.

timlautk/probability

Probabilistic reasoning and statistical analysis in TensorFlow

timlautk/PyTorch-Tutorial

Build your neural network easy and fast

timlautk/PyTorchZeroToAll

Simple PyTorch Tutorials Zero to ALL!

timlautk/Roadmap-of-DL-and-ML

Roadmap of DL and ML, some courses, study notes and paper summary

timlautk/SGDLibrary

Matlab library for stochastic gradient descent algorithms: Version 1.0.12

timlautk/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10

How to train a TensorFlow Object Detection Classifier for multiple object detection on Windows

timlautk/Tensorflow-Tutorial

Tensorflow tutorial from basic to hard

timlautk/tensorpack

A Neural Net Training Interface on TensorFlow

timlautk/tutorial-rl

timlautk/tutorials

timlautk/variational-inference-with-normalizing-flows

Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)