imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). (by csinva)
nn
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠 (by lab-ml)
Our great sponsors
| imodels | nn | |
|---|---|---|
| 7 | 26 | |
| 1,290 | 48,004 | |
| - | 8.5% | |
| 8.5 | 7.7 | |
| 5 days ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
imodels
Posts with mentions or reviews of imodels.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-31.
-
[D] Have researchers given up on traditional machine learning methods?
- all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications
-
What would be my best approach given the data I have?
Next, this variable will be your target and you can use various supervised learning models to answer your question. Since interpretation is key, you can use something from here: https://github.com/csinva/imodels or do some black box models and use shab to understand which features contributed most.
-
Random Forest Estimation Question
Option 2) fit a model from https://github.com/csinva/imodels on the predicted values of the RF
-
UC Berkeley Researchers Introduce ‘imodels: A Python Package For Fitting Interpretable Machine Learning Models
Despite recent breakthroughs in the formulation and fitting of interpretable models, implementations are frequently challenging to locate, utilize, and compare. imodels solves this void by offering a single interface and implementation for a wide range of state-of-the-art interpretable modeling techniques, especially rule-based methods. imodels is basically a Python tool for predictive modeling that is simple, transparent, and accurate. It gives users a straightforward way to fit and use state-of-the-art interpretable models, all of which are compatible with scikit-learn (Pedregosa et al., 2011). These models can frequently replace black-box models while boosting interpretability and computing efficiency without compromising forecast accuracy. Continue Reading
-
[D] Looking for open source projects to contribute
Our package imodels is expanding our sklearn-compatible set of interpretable models and always looking for new contributors!
- imodels: a package extending sklearn with state-of-the-art models for interpretable data science (e.g. Bayesian Rule Lists, RuleFit)
- imodels: a package extending sklearn with state-of-the-art interpretable models (e.g. Bayesian Rule Lists, RuleFit) from BAIR [P]
nn
Posts with mentions or reviews of nn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-09.
-
Can't remember name of website that has explanations side-by-side with code
Hey are you talking about https://nn.labml.ai/ ?
- [D] Recent ML papers to implement from scratch
-
[P] GPT-NeoX inference with LLM.int8() on 24GB GPU
Implementation & LM Eval Harness Results
-
[P] Fine-tuned the GPT-Neox Model to Generate Quotes
Github: https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/neox
-
Best resources to learn recent transformer papers and stay updated [D]
Regarding implementations this helps me: https://nn.labml.ai/
- Introductory papers to implement
- How to convert research papers to code?
-
[D] How to convert papers to code?
Dunno if this is directly helpful, but this website has implementation with the math side by side https://nn.labml.ai/
- [D] Looking for open source projects to contribute
- Resource for papers explanation
What are some alternatives?
When comparing imodels and nn you can also consider the following projects:
pycaret - An open-source, low-code machine learning library in Python
GFPGAN-for-Video-SR - A colab notebook for video super resolution using GFPGAN
interpret - Fit interpretable models. Explain blackbox machine learning.
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
shap - A game theoretic approach to explain the output of any machine learning model.
functorch - functorch is JAX-like composable function transforms for PyTorch.
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
ZoeDepth - Metric depth estimation from a single image
docarray - Represent, send, store and search multimodal data
onnx-simplifier - Simplify your onnx model
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.
imodels vs pycaret
nn vs GFPGAN-for-Video-SR
imodels vs interpret
nn vs labml
imodels vs shap
nn vs functorch
imodels vs linear-tree
nn vs ZoeDepth
imodels vs docarray
nn vs onnx-simplifier
imodels vs Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera
nn vs Basic-UI-for-GPT-J-6B-with-low-vram