/AI-for-Climate-Change

a collection of Jupyter notebooks and associated code that covers the fundamental concepts of deep learning and its application to climate change problems. This repository contains a range of materials to help you understand the basics of deep learning and its practical implementation using TensorFlow.

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

AI 4 Climate Bootcamp - Module 3: Deep Learning & TensorFlow

Binder Jupyter Notebook TensorFlow Python Transformers License: GPL v3

Welcome to the AI 4 Climate Bootcamp - Module 3: Deep Learning & TensorFlow! This repository contains materials to help you understand the fundamental concepts of deep learning and its practical implementation using TensorFlow.

Lessons Outline

Week 1: Introduction to Deep Learning and TensorFlow

In the first week, we reviewed data science and machine learning concepts and tools, and we introduced deep learning concepts and basic TensorFlow syntax. We covered the following topics:

  • Introduction to deep learning.
  • Building models using TensorFlow's Sequential API.
  • Dense layers.
  • Activation functions.
  • Loss functions and optimizers.

Week 2: Building User Interfaces and Reproducibility

In the second week, we went deeper into TensorFlow by learning about regularization techniques, building user interfaces for our models, and working on training monitoring and reproduction using Tensorboard. We covered the following topics:

  • Regularization techniques (dropout and early stopping).
  • Building user interfaces for models with Gradio.
  • Model reproducibility.
  • Training monitoring with Tensorboard.