## Transfer learning applied on the unsplash data using alexnet pretrained network (codes included)

Transfer learning using the pre-trained deep learning networks from MATLAB can be easily implemented to achieve fast and impressive results

Signal denoising using Fourier Analysis in Python (codes included)

We will learn the basics of Fourier analysis and implement it to remove noise...

Analyzing MiniSEED seismic data in MATLAB (codes included)

We will learn how to convert a mseed data file into mat format and then read ...

- Get Started
- Learn Git/Github for team collaboration
- Install Python via Anaconda
- Install Python IDE (Jupyter Notebook, Visual Studio Code)
- Python variables and data types
- Install and use Python packages
- Get familiar with
**Numpy**for data manipulations - Get familiar with
**Matplotlib**for plotting arrays - Get familiar with
**Pandas**to analyze tabular data

- Get familiar with text file formats
- Use basic Markdown syntax to format text in Jupyter Notebook files
- Get familiar with text file formats - CSV, .txt, YAML
- Read/Write data from text files using Python
- Read/Write data from text files using Pandas
- Deal with missing data in Pandas

- Spatial Data Analysis
- Read multi-layered raster data (.tif / .hdf / .nc) in Python
- Read vector data (shapefiles) using geopandas
- GIS in Python

- Time-series Analysis using Pandas
- Read time-series data
- Work with
`Datetime`

formats - Resample time series
- Filtering/smoothing time-series with Pandas
- Plot time series

- How to plot topographic high-resolution geospatial maps

Transfer learning using the pre-trained deep learning networks from MATLAB can be easily implemented to achieve fast and impressive results

In this introduction to the concepts of Pytorch data structures, we will learn about how to create and reshape tensors using Pytorch and compare it with the ...

We learn how to read huge csv file containing time series data by breaking it into chunks and then visualizing it with matplotlib

A PyQt5 application for retrieving and visualizing sound waveforms in real time. Codes included.

This tutorial gives a brief description of scientific computing using Pandas by introducing Series, DataFrame, Pandas common operations, methods, conditional...

This tutorial gives a brief description of scientific computing using numpy by introducing arrays, methods, attributes, random numbers, indexing, broadcastin...

I used the sktime library to forecast the airline data using NaiveForecaster, KNeighborsRegressor, Statistical forecasters, and auto ARIMA model.

We learn how to make the three-dimensional map using both GMT and PyGMT

An introduction to the wavelet analysis for a real geophysical data set. I compared the analysis to the Fourier analysis. Codes included!

GMT or generic mapping tools have become synonymous with plotting maps in Earth, Ocean, and Planetary sciences. It can be used for processing data, generatin...

What is the fastest and most efficient way to loop in Python. We found that the numpy is fastest and python builtins are the most memory efficient.

An introduction to the basics of genetic algorithm along with a simple numerical example and solution of an earthquake location problem

We will learn the basics of Fourier analysis and implement it to remove noise from the synthetic and real signals

We will learn how to convert a mseed data file into mat format and then read and analyze it using MATLAB

Transfer learning using the pre-trained deep learning networks from MATLAB can be easily implemented to achieve fast and impressive results

In this introduction to the concepts of Pytorch data structures, we will learn about how to create and reshape tensors using Pytorch and compare it with the ...

We learn how to read huge csv file containing time series data by breaking it into chunks and then visualizing it with matplotlib

A PyQt5 application for retrieving and visualizing sound waveforms in real time. Codes included.

This tutorial gives a brief description of scientific computing using Pandas by introducing Series, DataFrame, Pandas common operations, methods, conditional...

This tutorial gives a brief description of scientific computing using numpy by introducing arrays, methods, attributes, random numbers, indexing, broadcastin...

I used the sktime library to forecast the airline data using NaiveForecaster, KNeighborsRegressor, Statistical forecasters, and auto ARIMA model.

We learn how to make the three-dimensional map using both GMT and PyGMT

An introduction to the wavelet analysis for a real geophysical data set. I compared the analysis to the Fourier analysis. Codes included!

GMT or generic mapping tools have become synonymous with plotting maps in Earth, Ocean, and Planetary sciences. It can be used for processing data, generatin...

What is the fastest and most efficient way to loop in Python. We found that the numpy is fastest and python builtins are the most memory efficient.

An introduction to the basics of genetic algorithm along with a simple numerical example and solution of an earthquake location problem