Notebooks for Large Language Models (LLMs) Specialization

The official evaluation suite and dynamic data release for MixEval.

Lime: Explaining the predictions of any machine learning classifier

Source code/webpage/demos for the What-If Tool

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

A game theoretic approach to explain the output of any machine learning model.

XAI - An eXplainability toolbox for machine learning

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

Contains notebooks used in the Microsoft Azure Databricks Learning Paths modules.

The fastai book, published as Jupyter Notebooks

scikit-learn: machine learning in Python

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

An educational resource to help anyone learn deep reinforcement learning.

Python Implementation of Reinforcement Learning: An Introduction

Example code and materials that illustrate applications of SAS machine learning techniques.

Code samples and materials to help you learn to access SAS Viya services by writing programs in Python and other open-source languages.

All things SAS related (programs, macros, presentations, etc)

Example code and materials that illustrate applications of SAS machine learning techniques.

Caffe: a fast open framework for deep learning.

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials,…

A topic-centric list of HQ open datasets.