Pinned Repositories
alpha_vantage
A python wrapper for Alpha Vantage API for financial data.
Basic-Well-Log-Interpretation
Basic Well Log Interpretation with python, pandas, matplotlib
DataStructuresAlgorithms
500+ Data Structures and Algorithms practice problems
f18-06623
Fall 2018 - Mathematical Modeling of Chemical Engineering Processes
FinMathematics
Books
fipy
FiPy is a Finite Volume PDE solver written in Python
Geological-Log-Data-Machine-Learning-with-Python
A geological log data from a well in Kansas, USA is analyzed using Machine Learning (M.L.) techniques in Python. The data is overviewed, cleaned and analyzed for important patterns and relationships with which we found relationships of logs with each other and correlation of types of formations with the logs. Using this, we can eliminate the use of logs which are correlated or have no relative importance to the type of formation when we have prior geological knowledge of the area. Also, predictions of formation type were made successfully once the data is trained with the M.L. algorithms.
Leetcode_company_frequency
Collection of leetcode company tag problems. Periodically updating.
Monograph-20-Examples
Worked examples from Monograph 20 'Phase Behavior' - Appendices B and C
nghiabn.github.io
A beautiful, simple, clean, and responsive Jekyll theme for academics
nghiabn's Repositories
nghiabn/alpha_vantage
A python wrapper for Alpha Vantage API for financial data.
nghiabn/Basic-Well-Log-Interpretation
Basic Well Log Interpretation with python, pandas, matplotlib
nghiabn/DataStructuresAlgorithms
500+ Data Structures and Algorithms practice problems
nghiabn/f18-06623
Fall 2018 - Mathematical Modeling of Chemical Engineering Processes
nghiabn/FinMathematics
Books
nghiabn/fipy
FiPy is a Finite Volume PDE solver written in Python
nghiabn/Geological-Log-Data-Machine-Learning-with-Python
A geological log data from a well in Kansas, USA is analyzed using Machine Learning (M.L.) techniques in Python. The data is overviewed, cleaned and analyzed for important patterns and relationships with which we found relationships of logs with each other and correlation of types of formations with the logs. Using this, we can eliminate the use of logs which are correlated or have no relative importance to the type of formation when we have prior geological knowledge of the area. Also, predictions of formation type were made successfully once the data is trained with the M.L. algorithms.
nghiabn/Leetcode_company_frequency
Collection of leetcode company tag problems. Periodically updating.
nghiabn/Monograph-20-Examples
Worked examples from Monograph 20 'Phase Behavior' - Appendices B and C
nghiabn/nghiabn.github.io
A beautiful, simple, clean, and responsive Jekyll theme for academics
nghiabn/Books
Collection of Programming, Databases, Linux & Tools Books
nghiabn/Reinforcement-Learning-Cheat-Sheet
Reinforcement Learning Cheat Sheet
nghiabn/reservoir-simulation