Pinned Repositories
CNN_based_impedance_inversion
Convolutional neural network for seismic impedance inversion
d2geo
Framework for computing seismic attributes with Python.
ExcelNumericalDemos
A set of numerical demonstrations in Excel to assist with teaching / learning concepts in probability, statistics, spatial data analytics and geostatistics. I hope these resources are helpful, Prof. Michael Pyrcz
Force-2020-Machine-Learning-competition
the results, code and the data for the Force 2020 Machine learning competition after the completion of the competition in October 2020.
Functional-Python-Programming-Second-Edition
Functional Python Programming Second Edition, published by Packt
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Litho-prediction
This project attempts to predict lithology based on different well log response such as GR, Resistivity, NPHI, RHOB, etc.
papa_data1
Principal-Feature-Analysis
The first package for Principal Feature Analysis
Python-Object-Oriented-Programming---4th-edition
Code Repository for Python Object-Oriented Programming - 4th edition, Published by Packt
papsy88's Repositories
papsy88/spacex_dashboard
prpject for IBM certification
papsy88/Python-Object-Oriented-Programming---4th-edition
Code Repository for Python Object-Oriented Programming - 4th edition, Published by Packt
papsy88/Litho-prediction
This project attempts to predict lithology based on different well log response such as GR, Resistivity, NPHI, RHOB, etc.
papsy88/Principal-Feature-Analysis
The first package for Principal Feature Analysis
papsy88/RockPhysics_KeyLiteratures
A collection of key literatures in Rock Physics
papsy88/PythonNumericalDemos
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
papsy88/Force-2020-Machine-Learning-competition
the results, code and the data for the Force 2020 Machine learning competition after the completion of the competition in October 2020.
papsy88/handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
papsy88/papa_data1
papsy88/ExcelNumericalDemos
A set of numerical demonstrations in Excel to assist with teaching / learning concepts in probability, statistics, spatial data analytics and geostatistics. I hope these resources are helpful, Prof. Michael Pyrcz
papsy88/Functional-Python-Programming-Second-Edition
Functional Python Programming Second Edition, published by Packt
papsy88/d2geo
Framework for computing seismic attributes with Python.
papsy88/CNN_based_impedance_inversion
Convolutional neural network for seismic impedance inversion