sirnyls Goto Github PK

anchor

Code for "High-Precision Model-Agnostic Explanations" paper

autopersonalizationbench

blimey

Jupyter notebooks to reproduce various bLIMEy experiments

cam-keras

Class Activation Mapping visulaization

classification-cnn-medical-kaggle

Classifying medical images with a convolutional neural network

dalex

Descriptive mAchine Learning EXplanations

deepexplain

A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)

distracted-driver-detection

Predicting the likelihood of what the driver is doing in each of the pictures in the dataset.

distributed-data-distribution

explainingapproaches

Application of different explaining approaches for Neural Networks

fashion-mnist

A MNIST-like fashion product database. Benchmark :point_right:

gans

Generative Adversarial Networks implemented in PyTorch and Tensorflow

gpt-useridentity-inference

h20_ai_deeplearning_meetup_presentation

These are the slides on Interpretable Machine Learning with LIME framework I presented on H20's London meetup on AI and Deep Learning.

helloworld

first repository

image-classification

How to do Image Classification using Keras

innvestigate

A toolbox to iNNvestigate neural networks' predictions!

integrated-gradients

Attributing predictions made by the Inception network using the Integrated Gradients method

keras-grad-cam

An implementation of Grad-CAM with keras

keras-workshop

Data and scripts for keras course

l2x

lime

Lime: Explaining the predictions of any machine learning classifier

lime-experiments

Code for all experiments.

lime-text_data

Using LIME (Local Interpretable Model-Agnostic Explanations) for text data

loan-eligibility-lime

After using data on loan acceptances/rejections to train a neural net to predict likelihood of a loan being granted, local interpretable model-agnostic explanation (LIME) is used to explain the significant factors that lead to the model output.

lstm-text-generation

Tons of fun with text and recurrent neural networks! Let your computer read a book and tell you its own story. 🤣

scikit-learn

scikit-learn: machine learning in Python

shap

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

skater

Python Library for Model Interpretation/Explanations

task1