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ML Geo Curriculum
  • Machine Learning in the Geosciences

About this Book

  • Geosmart website
  • ML Project Overview
  • Acknowlegments

Chapter 1 - Open Source Ecosystem

  • Getting Started
  • 1.1 Open Reproducible Science
  • 1.3 Jupyter Environment
  • 1.3 Python Ecosystem
  • 1.4 Computing Environments
  • 1.5 Version Control & GitHub
  • 1.6 Data Gallery
  • Final Integrated Project in Machine Learning in Geoscience

Chapter 2 - Data Manipulation

  • Chapter Overview
  • 2.1 Data Definitions
  • 2.2 Data Formats
  • 2.3 Pandas
  • 2.4 DataFrame Exploration
  • 2.5 Data Arrays
  • 2.6 Resampling Methods
  • 2.7 Statistical Considerations for geoscientific Data and Noise
  • 2.8 Spectral Transforms
  • 2.9 Filtering Data
  • 2.10 Synthetic noise
  • 2.11 Feature Engineering
  • 2.12 Dimensionality Reduction
  • 2.13 ML-ready data
  • Assignment: Preparing AI-Ready Data for The Final Project

Chapter 3 - Machine Learning

  • Chapter Overview
  • 3.1 Concepts in training supervision
  • 3.2 Classification and Regression
  • 3.3 Clustering: Unsupervised Classification
  • 3.4 Binary classification
  • 3.5 Multiclass Classification
  • 3.6 Logistic regression
  • 3.7 Random Forests
  • 3.8 Robust Training
  • 3.9 Ensemble learning
  • 3.10 AutoML
  • Final Project - Classic Machine Learning
  • Homework Classic Machine Learning (50 points)

Chapter 4 - Deep Learning

  • Chapter Overview
  • 4.0 The Perceptron
  • 4.1 Neural Networks
  • 4.2 Multi Layer Perceptrons
  • 4.3 Convolutional Neural Networks
  • 4.4 Recurrent Neural Networks: Processing sequences
  • 4.5 Model Training
  • 4.6 Auto-encoders
  • 4.7 Physics-Informed Neural Networks
  • 4.8 NAS: Network Architecture Search
  • 4.9 LLMGEO
  • 4.10 Time Series Forecast
  • Deep Learning Exploration with AI-Ready Datasets

Chapter 5 - Workflow Management and Reproducibility

  • ML reproducibility

Chapter 6- Introduction to Cloud Computing

  • Browser Access to Cloud Instances
  • Terraform Access to Cloud Instances
  • AWS Cloud

Chapter 7 - MLLGEO Projects

  • Use Cases in MLGEO

Reference

  • Glossaries
  • Bibliography
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4.8 NAS: Network Architecture Search

4.8 NAS: Network Architecture Search#

The best model is one that fits the data best and can generalize the best to new examples. Provided these two metrics, ML practitioners experiment on model architectures that best satisfy these conditions. This can lead to a time-consuming effort to find the best model architecture.

A more systematic approach to finding the optimal model is to perform a Network Architecture Search, which is basically a consistent method to explore the “model space”, as in the “model architecture space”.

The field of NAS yields several packages to automate the searches.

In keras, this is the “https://keras.io/keras_tuner/” or “auto keras”

For pytorch, this is auto-pytorch.

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4.7 Physics-Informed Neural Networks

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4.9 LLMGEO

By eScience Institute, University of Washington
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