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This repository contains the Juptyter Notebooks described in the following Publication

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CC BY-NC-SA 4.0

Welcome to:
"Introduction to Artificial Intelligence"

This repository contains a collection of Jupyter Notebooks, which can be used to teach pharmaceutical and chemistry students the basics of Deep Learning. No prior coding knowledge is required. These Notebooks are on their own not sufficient to properly convey the knowledge, instructors need to prepare accompanying lectures. These notebooks are also not designed to teach students to train neural networks without any aid. Rather they aim to teach about the workings of neural networks through code completion.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

CC BY-NC-SA 4.0

This work was funded by the "Apotheker Stiftung Westfalen-Lippe"

Usage

  1. Goolge Colab
    The easiest way to use the Notebooks is to open them in Google Colab. The only thing needed is a Google Account. You can open a Juypter Notebook by simply clicking on a button in the table below. All notebooks will work out-of-the-box.

  2. Local Installation
    If you do not want to run the notebooks through a Google service, you can also setup your own local Python environment. We provide an instruction on how to do this. Like with Colab all notebooks will work straight aways, as soon as the loca installation has been completed.

Notebook English German
01. Introduction to Jupyter Open In Colab Open In Colab
02. Introduction to Python Open In Colab Open In Colab
03. Cheminformatics & RDKit Open In Colab Open In Colab
04. Linear Regression Open In Colab Open In Colab
05. Data Science Open In Colab Open In Colab
06. Linear Algebra Open In Colab Open In Colab
07. Your first Neural Network Open In Colab Open In Colab
08. PyTorch Open In Colab Open In Colab
09. Convolutional Neural Network Open In Colab Open In Colab
10. Transfer Learning Open In Colab Open In Colab
11. Recurrent Neural Networks Open In Colab Open In Colab
12. Autoencoders Open In Colab Open In Colab
13. Graph Neural Networks Open In Colab Open In Colab
14. Summary Open In Colab Open In Colab

Data Sources

Name Source
MNIST LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
BBBP Martins, I. F., et al. (2012) A Bayesian approach to in silico blood-brain barrier penetration modeling. Journal of Chemical Information and Modeling, 52(6), 1686-1697.
Pneumonia Kermany, D., Zhang, K., Goldbaum, M. (2018), Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images, Mendeley Data, V3, doi: 10.17632/rscbjbr9sj.3
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., ... & Zhang, K. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), 1122-1131.
Cats & Dogs Parkhi, O. M., Vedaldi, A., Zisserman, A., & Jawahar, C. V. (2012). Cats and dogs. In 2012 IEEE conference on computer vision and pattern recognition (pp. 3498-3505). IEEE.
GDB 11 Fink, T., & Reymond, J. L. (2007). Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. Journal of Chemical Information and Modeling, 47(2), 342-353.

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  • Jupyter Notebook 98.8%
  • Python 1.2%