PyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"

Code for IoT Journal paper 'ML-MCU: A Framework to Train ML Classifiers on MCU-based IoT Edge Devices'

Implementation of (overlap) local SGD in Pytorch

A compressed adaptive optimizer for training large-scale deep learning models using PyTorch

Lookahead optimizer ("Lookahead Optimizer: k steps forward, 1 step back") for tensorflow

Computer Vision and Image Processing algorithms implemented using OpenCV, NumPy and MatPlotLib, for UOM's EN2550 Fundamentals of Image Processing and Machine Vision Module ❄

Implement a Neural Network trained with back propagation in Python

Implement some ML algorithms in scala

Communication-efficient decentralized SGD (Pytorch)

Simple MATLAB toolbox for deep learning network: Version 1.0.3

Nadir is a library of bleeding-edge optimisers built for speed and functionality in PyTorch for researchers 💙

📈Implementing the ADAM optimizer from the ground up with PyTorch and comparing its performance on six 3-D objective functions (each progressively more difficult to optimize) against SGD, AdaGrad, and RMSProp.

基于粒子群PSO+随机梯度下降SGD优化器的Pytorch训练框架

ND-Adam is a tailored version of Adam for training DNNs.

A Repository to Visualize the training of Linear Model by optimizers such as SGD, Adam, RMSProp, AdamW, ASMGrad etc

Object recognition AI using deep learning

Tensorflow-Keras callback implementing arXiv 1712.07628

MetaPerceptron: Unleashing the Power of Metaheuristic-optimized Multi-Layer Perceptron - A Python Library

This was a project case study on nonlinear optimization. We implemented the Stochastic Quasi-Newton method, the Stochastic Proximal Gradient method and applied both to a dictionary learning problem.

In compressed decentralized optimization settings, there are benefits to having multiple gossip steps between subsequent gradient iterations, even when the cost of doing so is appropriately accounted for e.g. by means of reducing the precision of compressed information.