Pinned Repositories
applying-gradient-descent-data-science-intro-000
applying-gradient-descent-lab-data-science-intro-000
applying-nearest-neighbors-data-science-intro-000
architectural_styles
Version 1.2 of TensorFlow - US Architectural Style Image Classification
auto_sales_prediction
IN PROGRESS - Web Scraping, NLP(BERT), Random Forrest, Decision Trees, Linear Regression (Python, MongoDB)
byte
music_recommendation_systems
Recommendation system using content-based and collaborative-filtering (Surprise! and Amazon SageMaker)
nyc_restaurant_ratings
Web Scraping (Beautiful Soup), Database Creation (SQLAlchemy), Front-End Graphs (Dash, Flask)
predicting_wine_varietals
Web Scraping (Selenium), Natural Language Processing (NLP), Machine Learning (Scikit-Learn)
us_house_architecture
Image Classification for U.S. House Architectural Styles (CNN using TensorFlow on Google Cloud)
evanavaughan's Repositories
evanavaughan/auto_sales_prediction
IN PROGRESS - Web Scraping, NLP(BERT), Random Forrest, Decision Trees, Linear Regression (Python, MongoDB)
evanavaughan/us_house_architecture
Image Classification for U.S. House Architectural Styles (CNN using TensorFlow on Google Cloud)
evanavaughan/architectural_styles
Version 1.2 of TensorFlow - US Architectural Style Image Classification
evanavaughan/music_recommendation_systems
Recommendation system using content-based and collaborative-filtering (Surprise! and Amazon SageMaker)
evanavaughan/nyc_restaurant_ratings
Web Scraping (Beautiful Soup), Database Creation (SQLAlchemy), Front-End Graphs (Dash, Flask)
evanavaughan/codility_lessons
Codility Lesson1~Lesson17 100% solutions with Python3 / JavaScript ES2015+ - comment裡有解題的思考過程
evanavaughan/dsc-0-10-05-stats-learning-theory-nyc-career-ds-102218
evanavaughan/dsc-0-10-06-simple-linear-regression-nyc-career-ds-102218
evanavaughan/dsc-01-10-13-regression-statsmodels-lab-nyc-career-ds-102218
evanavaughan/dsc-04-41-04-introduction-to-keras-nyc-career-ds-102218
evanavaughan/dsc-04-41-05-keras-lab-nyc-career-ds-102218
evanavaughan/dsc-04-42-03-tuning-neural-networks-with-regularization-lab-nyc-career-ds-102218
evanavaughan/dsc-04-42-05-normalization-and-tuning-neural-networks-lab-nyc-career-ds-102218
evanavaughan/dsc-04-42-06-tuning-and-optimizing-neural-networks-lab-nyc-career-ds-102218
evanavaughan/dsc-04-43-02-convolutional-neural-networks-nyc-career-ds-102218
evanavaughan/dsc-04-43-03-convolutional-neural-networks-code-along-nyc-career-ds-102218
evanavaughan/dsc-04-43-03-convolutional-neural-networks-lab-nyc-career-ds-102218
evanavaughan/dsc-04-44-02-using-pretrained-networks-nyc-career-ds-102218
evanavaughan/dsc-04-44-03-using-pretrained-networks-codealong-nyc-career-ds-102218
evanavaughan/dsc-1-07-08-class-variables-and-instance-methods-nyc-career-ds-102218
evanavaughan/dsc-1-07-09-inheritance-nyc-career-ds-102218
evanavaughan/dsc-1-11-08-feature-scaling-and-normalization-nyc-career-ds-102218
evanavaughan/dsc-1-11-09-feature-scaling-and-normalization-lab-nyc-career-ds-102218
evanavaughan/dsc-1-11-10-multiple-linear-regression-in-statsmodels-nyc-career-ds-102218
evanavaughan/learning_log
evanavaughan/mod4_project
evanavaughan/nyc_property_values
Regression analysis of NYC property values
evanavaughan/nyc_restaurant_ratings1
evanavaughan/python-class-variables-class-methods-lab-nyc-career-ds-102218
evanavaughan/templates
sklearn templates