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18303

18.303 - Linear PDEs course

2021-zju-machine-learning

浙江大学机器学习

2022_khalighi_fdebenchmark

a-_guide_-to_data_sciecne_from_mathematics

It is a blueprint to data science from the mathematics to algorithms. It is not completed.

a-robust-real-time-polyp-detection

adaptive-des

adaptive-inertia-adai

The PyTorch Implementation of Adaptive Inertia Methods. The algorithms are based on the paper: "Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia".

adclassifiertorch

For more information, refer to https://www.frontiersin.org/articles/10.3389/fnins.2019.00509/full

advanced-probability-theory

记录本校高等概率论课程笔记

adventures-in-graph-theory

Sage source code for the computation of graphs and proofs from "Adventures in Graph Theory" by David Joyner and Caroline Grant Melles

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

aircraft_conception

The project is mainly for aircraft design and optimisation

aircraft_trajectory_optimisation

A simulation project using techniques in reinforcement learning to generate optimal paths for unmanned aircraft.

airlinetsp

Solving TSP with Gurobi

algotradingsimulatedpaths

Mean-Variance Portfolio Optimisation and Algorithmic Trading Strategies in MATLAB

alphatree-graphic-deep-neural-network

机器学习(Machine Learning)、深度学习(Deep Learning)、对抗神经网络(GAN），图神经网络（GNN），NLP，大数据相关的发展路书(roadmap), 并附海量源码（python，pytorch）带大家消化基本知识点，突破面试，完成从新手到合格工程师的跨越，其中深度学习相关论文附有tensorflow caffe官方源码，应用部分含推荐算法和知识图谱

alzheimer-disease-classification-from-eeg

This repo contains code for classification of alzheimer's disease from EEG signal.

alzheimer-s-classification-eeg

Alzheimer’s Disease (AD) is the most common neurodegenerative disease. It is typically late onset and can develop substantially before diagnosable symptoms appear. Electroencephalogram (EEG) could potentially serve as a noninvasive diagnostic tool for AD. Machine learning can be helpful in making inferences about changes in frequency bands in EEG data and how these changes relate to neural function. The EEG data was sourced from 2014 paper titled Alzheimer’s disease patients classification through EEG signals processing by Fiscon et al. There were patients with AD, mild cognitive impairment (MCI), and healthy controls. The data was already preprocessed using a fast fourier transform (FFT) to take the data from the time domain to the frequency domain. There were differing levels of effectiveness in terms of classification but generally, Fisher’s discriminant analysis (FDA), relevance vector machine, and random forest approaches were most successful. Due to inconsistent feature importances in different models, conclusions about important frequency bands for classification were not able to be made at this time. Similarly, different frequencies were not able to be localized to different regions of the brain. Further research is necessary to develop more interpretable models for classification.

amanpg

MATLAB implementation of AManPG

ann-optimization-using-pso-algorithm

Artificial Neural Network optimization using a Particle Swarm Optimization algorithm

ann-pso

Training Neural Network with Particle Swarm Optimization

annpso

Using Particle Swarm Optimization to train Neural Networks

apophysis-7x

A popular fork of Apohysis, a cosmic recursive fractal flame editor

artificial-neural-variability-for-deep-learning

The PyTorch Implementation of Variable Optimizers/ Neural Variable Risk Minimization. The algorithms are based on the original paper: Artificial Neural Variability for Deep Learning: On overfitting, Noise Memorization, and Catastrophic Forgetting.

ateam

A pyTorch Extension for Applied Mathematics

attentional_factorization_machine

TensorFlow Implementation of Attentional Factorization Machine

augmented-neural-odes

Pytorch implementation of Augmented Neural ODEs :sunflower:

awesome-capsule-networks

A curated list of awesome resources related to capsule networks

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

awesome-deep-learning-papers

The most cited deep learning papers