tcav 
 Concept explanation tool
 An interpretability method that provides explanations for neural network predictions by highlighting high-level concepts relevant to classification tasks.
Code for the TCAV ML interpretability project
633 stars
 34 watching
 151 forks
 
Language: Jupyter Notebook 
last commit: over 1 year ago   interpretabilitymachine-learningtcav 
 Related projects:
| Repository | Description | Stars | 
|---|---|---|
|    |  A library providing interpretability methods for TensorFlow 2.x models | 1,019 | 
|    |  An eXplainable AI toolkit for evaluating and interpreting neural network explanations in various deep learning frameworks. | 567 | 
|    |  Preprocessing and processing tools for text data in machine learning models | 1,239 | 
|    |  A tool to visually analyze and understand deep learning models' internal workings, specifically convolutional neural networks. | 780 | 
|    |  Evaluates and visualizes the performance of machine learning models. | 1,258 | 
|    |  Provides an implementation of Hierarchical explanations for Neural Network predictions | 127 | 
|    |  An open-source project that provides tools and utilities to understand how transformers are used in reinforcement learning tasks. | 75 | 
|    |  An end-to-end deep learning project demonstrating various TensorFlow techniques and applications in image classification, GANs, text classification, and more. | 1,113 | 
|    |  An implementation of a deep learning model for computer vision tasks using TensorFlow | 208 | 
|    |  Provides a method to generate explanations for predictions made by any black box classifier. | 798 | 
|    |  A toolkit for explaining complex AI models and data-driven insights | 1,641 | 
|    |  A library that enables distributed deep learning by partitioning tensors across processors in a mesh topology. | 1,597 | 
|    |  Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding. | 1,373 | 
|    |  Haskell bindings for a popular machine learning framework, allowing developers to build and deploy neural networks in the Haskell programming language. | 1,583 | 
|    |  An end-to-end platform for building and deploying machine learning applications | 186,822 |