The Earth-Life Science Institute at the Tokyo Institute of Technology held a Machine Learning and Deep Learning Bootcamp in collaboration with Machine Learning Tokyo for scientists in Biology, Chemistry, Astro- and Geophysics, and other fields researching the origins of life.


Nicholas Guttenberg

Suzana Ilić

Dimitris Katsios

Alisher Abdulkhaev


Find the MLT materials on GitHub:


ML Research Project Management

Intro to Deep Learning

Intro to Convolutional Neural Networks


Exploratory Data Analysis

Data Visualization

Train a Convolutional Neural Network

Data: Kaggle – DeepSat (SAT-6) Airborne Dataset
405,000 image patches each of size 28×28 and covering 6 landcover classes

  • Each sample image is 28×28 pixels with 4 bands – red, green, blue, near infrared.
  • The training and test labels are one-hot encoded 1×6 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 28×28 pixels.
  • Once generated, both the training and testing datasets were randomized using a pseudo-random number generator.


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