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.
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.
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.
In the third week, we learned about computer vision and how to build models for image classification and object detection. We covered the following topics:
- Introduction to computer vision.
- Image classification using CNNs.
- CNN optimization and regularization techniques.
- Transfer learning.
- Data pipelines.
In the fourth week, we learned about sequence modelling and how to build models for text classification and forecasting. We covered the following topics:
- Introduction to NLP.
- Sequence modelling with Recurrent Neural Networks (RNNs).
- Text Data Pipelines.
In the fifth week, we learned about advanced ML applications and how to build model and we covered the following topics:
- Forecasting with RNNs.
- Chatbots with Transformers.
- Object Detection with YOLOv5.