Official code tutorial for ACS In Focus (2024) - Neural Networks for Chemists
This e-book is designed as your first step in building an understanding of neural networks. Whether you are a student, researcher, or industry professional, it will equip you with the knowledge and tools to begin harnessing the power of neural networks in your own work.
The e-book begins with an exploration of the basic building blocks of neural networks. We then move onto fully connected networks, the most straightforward and foundational type of neural networks, as well as more advanced network architectures. Furthermore, we will explore case studies, discuss representation learning, and provide insights into how these tools are accelerating scientific discovery and transforming the field of biochemistry.
The code was tested in the following environment:
- python 3.11.9
- keras 3.5.0
- pytorch 2.4.0
- torchvision 0.19.0
- transformers 4.41.2
If you find Neural Networks for Chemists helpful, please consider citing us:
@book{acs2024neural,
title={Neural Networks for Chemists},
author={Xiao, Qingyang and Liu, Kaiyuan and Hong, Yuhui and Tang, Haixu},
year={2024},
publisher={American Chemical Society},
url={https://pubs.acs.org/doi/book/10.1021/acsinfocus.7e8012}
}