Pinned Repositories
BoF-ImageProcessingTutorial
BoF-MultiGPUTutorial
BoF_PINN
C-Cpp-Notes
Notes about modern C++, C++11, C++14 and C++17, Boost Libraries, ABI, foreign function interface and reference cards.
heart
Heart-Disease-Prediction-using-Machine-Learning Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate. The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and and predictions are made with different algorithms KNN, Decision Tree, Random Forest,SVM,Logistic Regression etc This is the jupyter notebook code and dataset I've used for my Kaggle kernel 'Binary Classification with Sklearn and Keras' I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not. Machine Learning algorithms used: Logistic Regression (Scikit-learn) Naive Bayes (Scikit-learn) Support Vector Machine (Linear) (Scikit-learn) K-Nearest Neighbours (Scikit-learn) Decision Tree (Scikit-learn) Random Forest (Scikit-learn) XGBoost (Scikit-learn) Artificial Neural Network with 1 Hidden layer (Keras) Accuracy achieved: 95% (Random Forest) Dataset used: https://www.kaggle.com/ronitf/heart-disease-uci
Learning-Python-Physics-Informed-Machine-Learning-PINNs-DeepONets
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
Machine_Learning_Fluid_Dynamics
A curated list of awesome Machine Learning projects in Fluid Dynamics
MultiGPU_Training_Examples
NekIBM
A Multiphase flow simulation platform using Direct-forced Immersed Boundary Method based on Spectral element solver Nek5000.
NekIBM-doc
NekIBM official doc
YunchaoYang's Repositories
YunchaoYang/Machine_Learning_Fluid_Dynamics
A curated list of awesome Machine Learning projects in Fluid Dynamics
YunchaoYang/NekIBM
A Multiphase flow simulation platform using Direct-forced Immersed Boundary Method based on Spectral element solver Nek5000.
YunchaoYang/BoF-ImageProcessingTutorial
YunchaoYang/BoF-MultiGPUTutorial
YunchaoYang/heart
Heart-Disease-Prediction-using-Machine-Learning Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate. The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and and predictions are made with different algorithms KNN, Decision Tree, Random Forest,SVM,Logistic Regression etc This is the jupyter notebook code and dataset I've used for my Kaggle kernel 'Binary Classification with Sklearn and Keras' I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not. Machine Learning algorithms used: Logistic Regression (Scikit-learn) Naive Bayes (Scikit-learn) Support Vector Machine (Linear) (Scikit-learn) K-Nearest Neighbours (Scikit-learn) Decision Tree (Scikit-learn) Random Forest (Scikit-learn) XGBoost (Scikit-learn) Artificial Neural Network with 1 Hidden layer (Keras) Accuracy achieved: 95% (Random Forest) Dataset used: https://www.kaggle.com/ronitf/heart-disease-uci
YunchaoYang/BoF_PINN
YunchaoYang/Learning-Python-Physics-Informed-Machine-Learning-PINNs-DeepONets
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
YunchaoYang/NekIBM-doc
NekIBM official doc
YunchaoYang/pytorch-cnn-visualizations
Pytorch implementation of convolutional neural network visualization techniques
YunchaoYang/Blogs
blogs and notes, https://yunchaoyang.github.io/blogs/
YunchaoYang/MultiGPU_Training_Examples
YunchaoYang/aihpc_notes
A minimal, responsive, and feature-rich Jekyll theme for technical writing.
YunchaoYang/CUDA-GEMM-Optimization
CUDA Matrix Multiplication Optimization
YunchaoYang/cuda-samples
Samples for CUDA Developers which demonstrates features in CUDA Toolkit
YunchaoYang/CUDA_Learning_Cliff
YunchaoYang/examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
YunchaoYang/Folder-Structure-Conventions
Folder / directory structure options and naming conventions for software projects
YunchaoYang/Foundation-Models-Physics
YunchaoYang/Getting-Things-Done-with-Pytorch
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.
YunchaoYang/github-slideshow
A robot powered training repository :robot:
YunchaoYang/ImageProcessing2023
YunchaoYang/leetcode
Provide all my solutions and explanations in Chinese for all the Leetcode coding problems.
YunchaoYang/LeetCode-1
leetcode的练习记录
YunchaoYang/Machine-Learning-Collection
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)
YunchaoYang/MultiGPU-NeMo-ptune
YunchaoYang/pytorch_distribute_tutorials
pytorch distribute tutorials
YunchaoYang/self-supervised-comparison
YunchaoYang/SLT-viewer-compare-tool
YunchaoYang/techblog
✨ Build a beautiful and simple website in literally minutes. Demo at https://beautifuljekyll.com
YunchaoYang/workbench-example-hybrid-rag
An NVIDIA AI Workbench example project for Retrieval Augmented Generation (RAG)