jaqueline-lu Goto Github PK

categorical-challenge-ii

Create neural network models (dense, bn, dropout) using Tensorflow backend (updated)

covid19-public-data

cs180-notes

face-counting-yolo

Use YOLOv3 pretrained model with Keras backend on counting faces in images

flowers-recognition

Using Tensorflow with EfficientNetB7, this models achieves 97% accuracy in classifying the 104 classes of flowers Trained with TPU

grand-valet

This is the repository for the project Grand Valet. The folder frontend contains the implementation of the frontend web application using React. The folder backend contains the implementation of the web server using Express. The test folder contains the unit tests for the backend, implemented with Python.

housing-on-rent-scrapy

Scrapy for housing on rent

jaqueline-lu.github.io

personal website

machine-learning-andrew-ng

the python version of the coursera version of Andrew Ng's machine learning course

nyc-taxi-prediction

Kaggle Competition predicting NYC taxi trip durations

secure-lora-middlebox

an innovative middlebox solution for LoRa cloud environments to enhance security and privacy in IoT deployments; Implemented features including traffic analysis, exposed key detection, device tracking, and data decryption

self-driving-car-obstacles-detection

Train YOLOv5 model on Self-Driving-Car datasets (about 15k)

super-resolution-for-mris

Comparison of various super resolution techniques for MRIs (T1 & T2)

titanic-prediction

Kaggle competitions Titanic Disaster Prediction