Olivier-FONTAINE's Stars
ColCarroll/ridge_map
Ridge plots of ridges
ashishpatel26/LLM-Finetuning
LLM Finetuning with peft
migariane/ColliderApp
Visualization Collider Effect: ShinyApp
gerdm/prml
Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop
SergeiVKalinin/MSE_Spring2024
The materials for the Spring Mathematics in Materials course at the UTK MSE
diviyank/SAM
Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)
nlp-with-transformers/notebooks
Jupyter notebooks for the Natural Language Processing with Transformers book
rguo12/awesome-causality-algorithms
An index of algorithms for learning causality with data
matteocourthoud/awesome-causal-inference
A curated list of causal inference libraries, resources, and applications.
PacktPublishing/Causal-Inference-and-Discovery-in-Python
Causal Inference and Discovery in Python by Packt Publishing
AlxndrMlk/causality
Notes, exercises and other materials related to causal inference, causal discovery and causal ML.
AnacletoLAB/grape
🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
msuzen/looper
A resource list for causality in statistics, data science and physics
matheusfacure/causal-inference-in-python-code
Code for the Book Causal Inference in Python
NielsRogge/Transformers-Tutorials
This repository contains demos I made with the Transformers library by HuggingFace.
oegedijk/explainerdashboard
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
lstehlik2809/Employee-Feedback-Analysis-Using-OpenAI
Python code for using GPT and embeddings from OpenAI for identifying topics and related sentiments in employee feedback
solegalli/machine-learning-imbalanced-data
Code repository for the online course Machine Learning with Imbalanced Data
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
jaryaman/causal-inference-cheat-sheet
Summary of useful results in Causal Inference
Ci2Lab/Applied_Causal_Inference_Course
This course is an overview of applied causal inference.
solegalli/hyperparameter-optimization
Code repository for the online course Hyperparameter Optimization for Machine Learning
awesomedata/awesome-public-datasets
A topic-centric list of HQ open datasets.
ashishpatel26/Amazing-Feature-Engineering
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
ali-vilab/composer
Official implementation of "Composer: Creative and Controllable Image Synthesis with Composable Conditions"
facebookresearch/ELI5
Scripts and links to recreate the ELI5 dataset.
SeldonIO/alibi
Algorithms for explaining machine learning models
huggingface/community-events
Place where folks can contribute to 🤗 community events
solegalli/feature-selection-for-machine-learning
Code repository for the online course Feature Selection for Machine Learning
Kanaries/pygwalker
PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis