Yunchao Yang's repositories
Machine_Learning_Fluid_Dynamics
A curated list of awesome Machine Learning projects in Fluid Dynamics
Learning-Python-Physics-Informed-Machine-Learning-PINNs-DeepONets
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
NekIBM-doc
NekIBM official doc
pytorch-cnn-visualizations
Pytorch implementation of convolutional neural network visualization techniques
cuda-samples
Samples for CUDA Developers which demonstrates features in CUDA Toolkit
examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Folder-Structure-Conventions
Folder / directory structure options and naming conventions for software projects
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.
github-slideshow
A robot powered training repository :robot:
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
leetcode
Provide all my solutions and explanations in Chinese for all the Leetcode coding problems.
LeetCode-1
leetcode的练习记录
Machine-Learning-Collection
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)
micro-service
sample micro-service in C++
nvidia-cuda-tutorial
Nvidia contributed CUDA tutorial for Numba
techblog
✨ Build a beautiful and simple website in literally minutes. Demo at https://beautifuljekyll.com
workbench-example-hybrid-rag
An NVIDIA AI Workbench example project for Retrieval Augmented Generation (RAG)
workshops
This is a repository for all workshop related materials.