dmol-book
shap
| dmol-book | shap | |
|---|---|---|
| 5 | 38 | |
| 582 | 21,759 | |
| - | 1.5% | |
| 3.4 | 9.3 | |
| 11 months ago | 4 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 or later | MIT License |
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.
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?
shap
- Shap v0.45.0
-
[D] Convert a ML model into a rule based system
something like GitHub - shap/shap: A game theoretic approach to explain the output of any machine learning model.?
-
[P] tinyshap: A minimal implementation of the SHAP algorithm
A less than 100 lines of code implementation of KernelSHAP because I had a hard time understanding shap's code.
-
What’s after model adequacy?
We use tools like SHAP to explain what the model is doing to stakeholders.
- Feature importance with feature engineering?
-
Model interpretation with many features
https://github.com/slundberg/shap this or https://github.com/marcotcr/lime would be relevant to you, especially if you want to look at explaining a single prediction.
-
SHAP Value Interpretation
See this closed topic for more detail: https://github.com/slundberg/shap/issues/29
-
Christoph Molnar on SHAP Library
Dr. Molnar recently had a semi-viral post on LinkedIn and on Twitter, where he essentially highlights the booming popularity [and power] of using SHAP for explainable AI (which I agree with), but that it also comes with problems; i.e., the open source implementation has thousands of pull requests, bugs, and issues and yet there is no permanent or significant funding to go in and fix them.
-
Random Forest Estimation Question
Option 4) create SHAP values https://github.com/slundberg/shap to better understand what the RF did.
-
Model explainability
txtai pipelines are wrappers around Hugging Face pipelines with logic to easily integrate with txtai's workflow framework. Given that, we can use the SHAP library to explain predictions.
What are some alternatives?
fastbook - The fastai book, published as Jupyter Notebooks
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
BestPractices - Things that you should (and should not) do in your Materials Informatics research.
Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
chemics-examples - Examples of using the Chemics package for Python
captum - Model interpretability and understanding for PyTorch
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
lime - Lime: Explaining the predictions of any machine learning classifier
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
interpret - Fit interpretable models. Explain blackbox machine learning.
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
anchor - Code for "High-Precision Model-Agnostic Explanations" paper