BestPractices
dmol-book
BestPractices | dmol-book | |
---|---|---|
1 | 5 | |
155 | 579 | |
- | - | |
2.8 | 3.4 | |
6 months ago | 10 months ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
BestPractices
-
Ideas for Materials Science / Data Science projects
My research group recently wrote the definitive best practice guide for getting into this field. We provide a GitHub with some example data that you could take a look at. The code snippets will be really useful because we have them really well commented. https://github.com/anthony-wang/BestPractices
dmol-book
-
Best machine learning course for academic research?
A chemical engineering professor of mine wrote an open-sourse online book that I really enjoyed. dmol.pub
- [Deep Learning] deep learning for molecules & materials
-
If you believe like Eliezer Yudkowsky that superintelligent AI is threatening to kill us all, why aren't you evangelizing harder than Christians, why isn't it the main topic talked about in this subreddit or in Scott's blog, why aren't you focusing working only on it?
This is a good time to learn about machine learning if you're a chemist. This book, "deep learning for molecules & materials" by Andrew White, was recommended to me by a friend in the field: https://dmol.pub.
-
Deep neural networks and autoencoders
Andrew White’s online book Deep Learning for Molecules (https://dmol.pub) is a great start if you have some coding Python experience, it has it’s own github (i.e., code examples) as wel as a chapter on variational autoencoders.
- How to get started with molecular discovery?
What are some alternatives?
nbdev - Create delightful software with Jupyter Notebooks
fastbook - The fastai book, published as Jupyter Notebooks
fastpages - An easy to use blogging platform, with enhanced support for Jupyter Notebooks.
shap - A game theoretic approach to explain the output of any machine learning model.
py - Repository to store sample python programs for python learning
chemics-examples - Examples of using the Chemics package for Python
pymatgen - Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
reinforcement_learning_course_materials - Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
jupytemplate - Templates for jupyter notebooks
jupyter-memgraph-tutorials - Learn to use Memgraph and GQLAlchemy quickly with the help of Jupyter Notebooks