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Tung Thanh Le's Projects

30daysofcode_hackerrank icon 30daysofcode_hackerrank

Hackerrank provides a lot of interviewing questions that could help to prepare for the coding interviews.

aas icon aas

Code to accompany Advanced Analytics with Spark from O'Reilly Media

ampl-cplex-optimization-solver icon ampl-cplex-optimization-solver

Modeled a system with task scheduling problem as a max-sat problem and translated it into the integer linear programming (ILP) problem. Used the optimization solvers (CPLEX, Gurobi, Xpress) to solve the problem.

annotated_deep_learning_paper_implementations icon annotated_deep_learning_paper_implementations

🧑‍🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, etc. 🧠

aws-ai-qna-bot icon aws-ai-qna-bot

Code samples related to "Creating a Question and Answer Bot with Amazon Lex and Amazon Alexa", published on the AWS AI Blog. QnABot (pronounced “Q and A Bot”), uses Amazon Lex and Amazon Alexa to provide a conversational interface to your “Questions and Answers”, so users can just ask their questions and get quick and relevant answers.

bias-variance-tradeoff icon bias-variance-tradeoff

Sandbox for generating visualizations of the bias-variance tradeoff for Machine Learning at Berkeley's blog.

carnd-vehicle-detection icon carnd-vehicle-detection

My solution to the Udacity Self-Driving Car Engineer Nanodegree Vehicle Detection and Tracking project.

causalml icon causalml

Uplift modeling and causal inference with machine learning algorithms

channel-attribution-model icon channel-attribution-model

An attention-based Recurrent Neural Net multi-touch attribution model in a supervised learning fashion of predicting if a series of events leads to conversion (purchase). The trained model can also assign credits to channels. The model also incorporates user-context information, such as user demographics and behavior, as control variables to reduce the estimation biases of media effects.

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