Pinned Repositories
ASEBO
Code to run the ASEBO algorithm from the paper: From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization... please get in touch if interested!!
Berkeley-AI-Pacman-Projects
My solutions to the UC Berkeley AI Pacman Projects
coding-interview-university
A complete computer science study plan to become a software engineer.
CS106.K21.KHTN
Fruits-Classification-in-Android
Capstone Project: Fruits Classification in Android
Introduction-to-Machine-Learning-with-Python
IT007.K21.KHTN
KDDCup2021-CityBrainChallenge-starter-kit
Machine_Learning_Coursera
UIT-ALGO-BOOTCAMP
KhangTran2503's Repositories
KhangTran2503/IT007.K21.KHTN
KhangTran2503/CS106.K21.KHTN
KhangTran2503/Fruits-Classification-in-Android
Capstone Project: Fruits Classification in Android
KhangTran2503/Introduction-to-Machine-Learning-with-Python
KhangTran2503/KDDCup2021-CityBrainChallenge-starter-kit
KhangTran2503/Machine_Learning_Coursera
KhangTran2503/UIT-ALGO-BOOTCAMP
KhangTran2503/ASEBO
Code to run the ASEBO algorithm from the paper: From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization... please get in touch if interested!!
KhangTran2503/coding-interview-university
A complete computer science study plan to become a software engineer.
KhangTran2503/compression
KhangTran2503/cpbook-code
CP4 Free Source Code Project (C++17, Java11, Python3 and OCaml)
KhangTran2503/CS112.L11.KHTN
KhangTran2503/CS97SI
KhangTran2503/deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
KhangTran2503/deepLearningBook-Notes
Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)
KhangTran2503/Evolutionary-Computing
KhangTran2503/foundational_learning_inference
Mathematical foundations of learning and inference
KhangTran2503/LaTeX-Workshop
Boost LaTeX typesetting efficiency with preview, compile, autocomplete, colorize, and more.
KhangTran2503/learn-machine-learning-in-two-months
Những kiến thức cần thiết để học tốt Machine Learning trong vòng 2 tháng. Essential Knowledge for learning Machine Learning in two months.
KhangTran2503/lich.thi.csv.googlecalendar
KhangTran2503/mml-book.github.io
Companion webpage to the book "Mathematics For Machine Learning"
KhangTran2503/pderl
Code for "Proximal Distilled Evolutionary Reinforcement Learning", accepted at AAAI 2020
KhangTran2503/PGMORL
[ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
KhangTran2503/pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
KhangTran2503/PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
KhangTran2503/reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
KhangTran2503/Simulation_experiments_for_optimizing_objective_function
Simulation experiments for optimizing objective function with Differential Evolution, Evolution Strategies and Cross Entropy Method (2 versions)
KhangTran2503/T-414-AFLV
T-414-ÁFLV: A Competitive Programming Course
KhangTran2503/Train-ICPC
KhangTran2503/UIT-ICPC-Template-Latex