https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp

Intro to Machine Learning and Deep Learning for Earth-Life Sciences
https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp

Last synced: 3 months ago
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Intro to Machine Learning and Deep Learning for Earth-Life Sciences

• Host: GitHub
• URL: https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp
• Owner: Machine-Learning-Tokyo
• Created: 2019-06-15T02:40:22.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-06-29T08:35:30.000Z (over 5 years ago)
• Last Synced: 2024-04-17T07:58:50.722Z (7 months ago)
• Language: Jupyter Notebook
• Size: 20 MB
• Stars: 14
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• Awesome-Earth-Artificial-Intelligence - ELSI-DL-Bootcamp - Intro to Machine Learning and Deep Learning for Earth-Life Sciences, (Tutorials)

README

        
# ELSI-DL-Bootcamp

Intro to Machine Learning and Deep Learning for Earth-Life Sciences

## Slides

### [ML Research Project Management](https://docs.google.com/presentation/d/1y4v1WdDILWbbqPQzEO8W4v33dVoCFl5I_04dTFyJZoE/edit?usp=sharing)

### [Intro to Deep Learning](https://docs.google.com/presentation/d/1V-O6DAKWkRUGpBT2PvB5LQ2X1BJMUOxwZupKhLQpXb8/edit?usp=sharing)

### [Intro to Convolutional Neural Networks](https://docs.google.com/presentation/d/1Z27oJAUO_mUQWcZDyl5nu4MLPk7FF8ggeAqLdyOrIMU/edit?usp=sharing)

## Notebooks

### [Exploratory Data Analysis](https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp/blob/master/Data_Exploration.ipynb)

### [Data Visualization](https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp/blob/master/data_visualization.ipynb)

### [Train a Convolutional Neural Network](https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp/blob/master/kaggle_sat.ipynb)

**Data**: Kaggle - [DeepSat (SAT-6) Airborne Dataset](https://www.kaggle.com/crawford/deepsat-sat6)

405,000 image patches each of size 28x28 and covering 6 landcover classes

**Content**

- Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared.

- The training and test labels are one-hot encoded 1x6 vectors

- The six classes represent the six broad land covers which include barren land, trees, grassland, roads, buildings and water bodies.

- Training and test datasets belong to disjoint set of image tiles.

- Each image patch is size normalized to 28x28 pixels.

- Once generated, both the training and testing datasets were randomized using a pseudo-random number generator.

[
](https://www.kaggle.com/crawford/deepsat-sat6)