imodels
bootcamp
Our great sponsors
imodels | bootcamp | |
---|---|---|
7 | 24 | |
1,290 | 1,619 | |
- | 3.6% | |
8.5 | 9.1 | |
5 days ago | 4 days ago | |
Jupyter Notebook | HTML | |
MIT License | Apache License 2.0 |
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
-
[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]
bootcamp
- FLaNK AI - 01 April 2024
- FLaNK Stack Weekly 22 January 2024
-
Milvus Adventures Jan 5, 2023
Metadata Filtering with Zilliz Cloud Pipelines This tutorial discuss scalar or metadata filtering and how you can perform metadata filtering in Zilliz Cloud. This blog continues on the previous blog on Getting started with RAG in just 5 minutes. You can find its code in this notebook and scroll down to Cell #27.
-
Build a search engine, not a vector DB
Partially agree.
Vector DBs are critical components in retrieval systems. What most applications need are retrieval systems, rather than building blocks of retrieval systems. That doesn't mean the building blocks are not important.
As someone working on vector DB, I find many users struggling in building their own retrieval systems with building blocks such as embedding service (openai,cohere), logic orchestration framework (langchain/llamaindex) and vector databases, some even with reranker models. Putting them together is not as easy as it looks. A fairly changeling system work. Letting alone quality tuning and devops.
The struggle is no surprise to me, as tech companies who are experts on this (google,meta) all have dedicated teams working on retrieval system alone, making tons of optimizations and develop a whole feedback loop of evaluating and improving the quality. Most developers don't get access to such resource.
No one size fits all. I think there shall exist a service that democratize AI-powered retrieval, in simple words the know-how of using embedding+vectordb and a bunch of tricks to achieve SOTA retrieval quality.
With this idea I built a Retrieval-as-a-service solution, and here is its demo:
https://github.com/milvus-io/bootcamp/blob/master/bootcamp/R...
Curious to learn your thoughts.
-
Vector Database in a Jupyter Notebook
Although it's common to use vector databases in conjunction with LLMs, I like to talk about vector databases in the context of unstructured data, i.e. any data that you can vectorize with (or without) an ML model. Yes, this includes text, but it also includes things like visual data, molecular structures, and geospatial data.
For folks who want to learn a bit more, there are examples of vector database use cases beyond semantic text search in our bootcamp: https://github.com/milvus-io/bootcamp
-
Beginner-ish resources for choosing a vector database?
Easy to get started: Here are some tutorials for Milvus in a Jupyter Notebook that I wrote - reverse image search, semantic text search
-
Semantic Similarity Search
I think you can just store your vector embeddings in the vector store somewhere and then query with your second document. I created a short tutorial on this that shows how to get the top 2 vector embeddings from a text query
-
[D] Looking for open source projects to contribute
For more beginner tasks associated with the Milvus vector database, you can contribute to the Bootcamp project( https://github.com/milvus-io/bootcamp), where we build a lot of data-driven solutions using ML and Milvus vector database, including reverse image search, recommender systems, etc.
-
I built an image similarity search system... Suggestions needed: what are some fun image datasets or scenarios I can use with this? :)
Source code here: https://github.com/milvus-io/bootcamp/tree/master/solutions/reverse_image_search
- Faiss: Facebook's open source vector search library
What are some alternatives?
pycaret - An open-source, low-code machine learning library in Python
Milvus - A cloud-native vector database, storage for next generation AI applications
interpret - Fit interpretable models. Explain blackbox machine learning.
google-research - Google Research
shap - A game theoretic approach to explain the output of any machine learning model.
docarray - Represent, send, store and search multimodal data
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
es-clip-image-search - Sample implementation of natural language image search with OpenAI's CLIP and Elasticsearch or Opensearch.
habitat-sim - A flexible, high-performance 3D simulator for Embodied AI research.
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk