From
2018/5/12 v1
2019/7/11 v2

1. Named Entity Recognition
   • Compared with different NN structures, incl. Bi-LSTM, CNN+LSTM – F1 score 83.2%
   • Utilised different embedding function, incl. Glove and Tensorflow Elmo

2. Sentiment Classification NLP
   • Compared with different structures, incl. Bi-LSTM + Global Soft Attention and SVM – Acc 0.771
   

3. Speech Recognition Industrial Project
   • Utilised Bi-LSTM to create language speech recognition system transforming audio data to text
   • Audio → MFCC → NN → Target: CTC Loss (Beam Search, Edit Distance)

4. Kaggle - House Prices: Advanced Regression Techniques
   • Achieved top 6% and accuracy of 0.11 for the RMSE
   • DNN, Linear Regression & Stacking Model (Lasso, Elastic Net, SVR, Kernel Ridge, Bayesian Ridge, Ridge)

5. Kaggle - Histopathologic Cancer Detection
   • Two CNN structures, incl. Fastai DenseNet 201 & NASNet + global max/average pooling – Acc 95.9%

6. Machine Learning Mobile Application – Second Hand Car Selling
   • Model 1: Utilised NN to create a price prediction system
   • Model 2: Utilised Latent Dirichlet Allocation to create a topic system
   • Model 3: Utilised Google Cloud Vision API to create an image recognition system
   • Workflow: Created ML models (Python) → Imported 3 models to API → Created a mobile application

7. Machine Learning Startup Project – Crime Warning System
   • Market Demand: Theft offences occurred at a rate of 45.8 per 1,000 population in 2017 in London
   • Workflow: Dimension Reduction → ARIMA & LSTM → Validation → Output Prediction
   • Function: Once users walks on a road at a specific time, they will be reminded if the time is dangerous

8. Kaggle – Titanic Machine Learning from Disaster
   • Achieved top 2% and accuracy of 83.7% for the prediction of the data
   • Utilised 2 methods to predict labels respectively, incl. DNN & Random Forest