Kuan Wei's repositories
Choose-best-Sephora-Make-up-Products-With-A-Limited-Budget
This repository contains the script for infinite scroll web page scraping developed by myself, and a practical project which uses this scraping code for collecting product information from Sephora and select best combinations of new make-up within a given budget of 100 dollars.
Chinook-Music-Store-Data-Analysis-Using-SQL
Chinook database is a media-related database that contains data about Chinook Music Store. The database contains 11 different tables: album, artist, customer, employee, genre, invoice, invoice_line, media_type, playlist, playlist_track, and track. In this project I will apply my SQL skills to answer several business-related questions.
Data-Project-How-Do-Education-and-Income-Affect-Marriage-and-Divorce-Rates
This project studies the correlations between marriage rate, divorce rate and income, education, to try to understand some conflicts in the perspectives of marriage in between our generation and our parent's generation
Understand-Transposed-Convolutions-And-Build-Conv2DTranspose-Layer-From-Stratch
In generative adversarial network (GAN), convolutions and transposed convolutions are both heavily involved. While convolutions play an important role in the discriminator, transposed convolutions are the primary building blocks for the generator. The tensorflow API - Keras - has made building GAN a very convenient process. However, sometimes it can be confusing of **what values should be used for the kernel size, strides, and padding to yield the right output shapes.** Setting the right values for the parameters require us to understand how transposed convolutions work. In this notebook, I would like to share some of my personal understandings about transposed convolutions. Throughout the notebook, I will use convolutions as the comparison to better explain transposed convolutions. I will also show you how I implement these understanding to build my own convolutional and transposed convolutional layers, which act as a naive version of the Conv2D and Conv2DTranspose layers from Keras. The notebook consists of three sections: #### 1. What are transposed convolutions? #### 2. How do the parameters (kernel size, strides, and padding) affect transposed convolutions? #### 3. Build my own Conv2D and Conv2DTranspose layers from scratch
Kaggle-Titanic-Competition
Titanic: Machine Learning from Disaster is a famous Kaggle competition in which participants are asked to build machine learning models to predict if a passenger will survive or not. It is a great resource for practicing data analysis skills for self-learning students like myself. In this notebook, I present how I handle the dataset and eventually build a model with a testing dataset prediction accuracy of 78.7%.