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

Communication-efficient decentralized SGD (Pytorch)

Implement some ML algorithms in scala

Simple MATLAB toolbox for deep learning network: Version 1.0.3

📈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.

Nadir: Cutting-edge PyTorch optimizers for simplicity & composability! 🔥🚀💻

基于粒子群PSO+随机梯度下降SGD优化器的Pytorch训练框架

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

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

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.

It's designed to take you on a journey through the fundamental principles and applications of Linear Regression.