Name: Jiew Wan Tan
Type: User
Company: Intersect Insight
Bio: Systems architect trained in data science & quantitative analysis. Generates solutions to business and investment problems using advanced learning algorithms.
Location: Singapore & Kuala Lumpur, MY
Blog: https://www.linkedin.com/in/jiewwantan/
Jiew Wan Tan's Projects
Worldwide antimicrobial resistance data visualization using data-driven document D3.js animating from 1987 till 2014
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
Banana collection navigation using a vanilla DQN
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
Lane Finding Project for Self-Driving Car ND
TensorFlow Lab for Self-Driving Car ND
Predicting music streaming service user churn on local machine and AWS EMR using Apache Spark.
Collaboration and competition of two tennis playing agents with DDPG
A continuous control environment solved with DDPG actor-critic agent
This program allows to choose a stock index from 5 stock indices and explore the chosen index correlations with the other 4 indices.
Modularized Implementation of Deep RL Algorithms in PyTorch
A web app to analyse and categorizes disaster response message with ETL, NLP and ML pipelines
Financial Data exploratory analysis using R: Distribution, time-series analysis, bivariate exploration of various features, correlation matrix, risk sentiments and behavior for different asset class.
A toolkit for developing and comparing reinforcement learning algorithms.
This project deploys a High-Availability Web Application Using AWS CloudFormation Infrastructure-as-code (IAC) that automates the process of creating a secured and high-availability environment and deploying an application (packaged and staged in AWS S3 Storage) into a dockerized Apache Web Server. The script contains all the configurations needed for a repeatable process so that the infrastructure can be discarded and recreated at will multiple times.
Content for Udacity's Machine Learning curriculum
Models built with TensorFlow
This project loads the USDA food database in json format and parses the data into components for analysis in a time and computation efficient manner.
The program executes trading on a wide spectrum of position sizing and allocation strategies based on two choices of EMA signal EMA 21 and 45 +/- 0.5 ATR trade strategies. The combination of these creates 18 trade strategies.
This project sets to design a framework for property market analysis in a city with the objective of identifying property that generates rental income in Airbnb marketplace.
This project analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles that they will like.
Recoverability in AWS
Python Implementation of Reinforcement Learning: An Introduction
Backtesting with varied risk management strategies