📋 Content
- 📋 Content
- 📄 Description
- 📣 Current announcements
- ❗ Course requirements
- 📒 Syllabus
- 📝 Theoretical basics
- ☝️ References
📄 Description
This directory contains a few basic notebooks to expand your deep learning knowledge. Working with Keras and TensorFlow, you’ll learn about neural networks, the deep learning model workflows, and how to optimize your models.
By the end of these notebooks, you should feel more comfortable with:
- with the basics and the terms of DeepLearning
- to use Keras to train and test models
📣 Current announcements
In this skill track, the notebooks build on each other. Therefore, complete them in the order given!
❗ Course requirements
- you should have completed all previous skill tracks
- in this notebook we use data from open-source databases (the references are at the bottom of the page); in Google Colab the data is loaded automatically at the beginning of the notebook
- have a look at the theoretical basics before you start with the notebooks
📒 Syllabus
- Basic Concepts of DeepLearning
- Basic Concepts of Keras
- MedNIST Dataset
📝 Theoretical basics
For some information on the topic, take a look at some Basics.
☝️ References
In this skill track, you’ll work with different Open-Source datasets:
- MedMNIST [1,2]
[1] Jiancheng Yang et al. (2023) MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification. Scientific Data
[2] Jiancheng Yang et al. (2021), MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis, IEEE 18th International Symposium on Biomedical Imaging (ISBI)