Pinned Repositories
Articles
References to the Medium articles
awesome-deep-learning
A curated list of awesome Deep Learning tutorials, projects and communities.
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
CS224n-Reading-Notes
CS224n Reading Notes in Chinese 中文阅读笔记
CS6190
CS6350-Machine-Learning
DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
Eric-Woo
ericwu.github.io
Tesla-stock-price-prediction-
This project was completed with the intention of helping Tesla stock investors better understand how to make decisions where the stock market is very volatile by training different models through historical and social media data analytics. Behavioral economics shows that public emotions can profoundly affect individual behavior and decision making. In order for investors to utilize it, business analysts must understand the behaviors and attitudes of the public within the finance context. Nowadays, social media perfectly tracked by data reflects the public emotions and sentiment about stock movement. Also, tremendous stock marketing news can be used to capture a trend of stock movement. The fundamental trading and decision making for main techniques rely on expert training and prediction. This article concentrated on tweets and stock news, and I applied sentiment analysis and machine learning models, especially, XGBoost to tweets and news extracted from Elon Musk tweets, Nasdaq and New York Times News about Tesla. Only by understanding the values and priorities of the public sentiment of Tesla stock will investors be able to make significant decisions. In addition, I conducted two models- ARIMA and RNN(LSTM) in forecasting the Tesla stock price. I compare their results with the prediction performances of the classical ARIMA and RNN.
Eric-Woo's Repositories
Eric-Woo/Tesla-stock-price-prediction-
This project was completed with the intention of helping Tesla stock investors better understand how to make decisions where the stock market is very volatile by training different models through historical and social media data analytics. Behavioral economics shows that public emotions can profoundly affect individual behavior and decision making. In order for investors to utilize it, business analysts must understand the behaviors and attitudes of the public within the finance context. Nowadays, social media perfectly tracked by data reflects the public emotions and sentiment about stock movement. Also, tremendous stock marketing news can be used to capture a trend of stock movement. The fundamental trading and decision making for main techniques rely on expert training and prediction. This article concentrated on tweets and stock news, and I applied sentiment analysis and machine learning models, especially, XGBoost to tweets and news extracted from Elon Musk tweets, Nasdaq and New York Times News about Tesla. Only by understanding the values and priorities of the public sentiment of Tesla stock will investors be able to make significant decisions. In addition, I conducted two models- ARIMA and RNN(LSTM) in forecasting the Tesla stock price. I compare their results with the prediction performances of the classical ARIMA and RNN.
Eric-Woo/CS6350-Machine-Learning
Eric-Woo/Articles
References to the Medium articles
Eric-Woo/awesome-deep-learning
A curated list of awesome Deep Learning tutorials, projects and communities.
Eric-Woo/awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
Eric-Woo/CS224n-Reading-Notes
CS224n Reading Notes in Chinese 中文阅读笔记
Eric-Woo/CS6190
Eric-Woo/DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
Eric-Woo/Eric-Woo
Eric-Woo/ericwu.github.io
Eric-Woo/Hello-World
just a repository
Eric-Woo/mgcnn
Multi-Graph Convolutional Neural Networks
Eric-Woo/Millennials-investment-and-some-misconception-analysis
Eric-Woo/ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
Eric-Woo/nlp-tutorial
Natural Language Processing Tutorial for Deep Learning Researchers
Eric-Woo/pml-book
"Probabilistic Machine Learning" - a book series by Kevin Murphy
Eric-Woo/PRML_learning
learning fomula
Eric-Woo/pumpkin-book
《机器学习》（西瓜书）公式推导解析，在线阅读地址：https://datawhalechina.github.io/pumpkin-book
Eric-Woo/pytorch-book
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)
Eric-Woo/US-Car-Severity-predition_New-York-City
Identifying the circumstances under what features affect accident severity and which areas have high accident rates can help insurance companies predict premium and budget of expenditure. To understand factors influencing the severity of US car accidents, including location and weather-related conditions, several classification models were developed. This project focus on the prediction of US car accident severity using the Decision Tree, Random Forest, K-Nearest Neighbors, Logistic Regression, and two popular tree-based ensemble methods, XGBoost and LightGBM. In the process, we first try PCA and then perform Grid-search and Cross-validation to tune the parameters. In order to solve the spatial heterogeneity and imbalanced data problems, we conduct the analysis of the whole United States and New York City with the resampling method. We finally make some comparations about methods and models we used to find a better way to predict the US car severity.