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kmario23 / deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

machine-learning natural-language-processing deep-neural-networks reinforcement-learning computer-vision deep-learning optimization probability deep-reinforcement-learning medical-imaging speech-recognition artificial-neural-networks pattern-recognition probabilistic-graphical-models bayesian-statistics artificial-intelligence-algorithms visual-recognition geometric-deep-learning explainable-ai graph-neural-networks

Updated Apr 10, 2022
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mindsdb / mindsdb

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[New Integration]: Store files in AWS S3 and Google Cloud Storage

EZFRICA commented Jul 1, 2022

Is there an existing integration?

I have searched the existing integrations.

Use Case

This will allow to host the software on a serverless and stateless service. This will facilitate scaling because no critical data will be on the disk of the system running the application.

Motivation

Allow users to save resources and money.

Implementation

No response

Anything e

enhancement help wanted good first issue integration

Open

[New Integration]: Integration for Crime Analytics and Prediction with MindsDB

Open

[New Integration]: Integrate Elasticsearch and extend Tracardi inference process :+1: :+1: :+1:

2

Find more good first issues

Advanced AI Explainability for computer vision. Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Examples and applications for classification, object detection, segmentation, embedding networks and more. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM, LayerCAM, FullGrad and XGrad-CAM. Evaluation metrics.

deep-learning grad-cam pytorch visualizations interpretability class-activation-maps interpretable-deep-learning interpretable-ai explainable-ai xai score-cam vision-transformers

Updated Jul 9, 2022
Python

interpretml / interpret

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Interpret

5

wh3nd1g0h commented Jul 29, 2020

Yes

good first issue

microsoft / tensorwatch

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How to use Watcher / WatcherClient over tcp/ip network? (ex. cloud - local)

2

tanabata-kitajima commented Jul 3, 2019

How to use Watcher / WatcherClient over tcp/ip network?

Watcher seems to ZMQ server, and WatcherClient is ZMQ Client, but there is no API/Interface to config server IP address.
Do I need to implement a class that inherits from WatcherClient?

enhancement good first issue

AstraZeneca / awesome-explainable-graph-reasoning

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A collection of research papers and software related to explainability in graph machine learning.

deep-learning graph graph-algorithms awesome-list graphml explainable-ai explainable-ml

Updated Apr 4, 2022

Trusted-AI / AIX360

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Interpretability and explainability of data and machine learning models

machine-learning deep-learning artificial-intelligence ibm-research explainable-ai explainable-ml xai ibm-research-ai codait trusted-ai trusted-ml explainabil

Updated Jul 3, 2022
Python

ModelOriented / DALEX

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moDel Agnostic Language for Exploration and eXplanation

black-box data-science machine-learning predictive-modeling fairness interpretability explainable-artificial-intelligence explanations explainable-ai explainable-ml xai model-visualization interpretable-machine-learning iml dalex responsible-ai responsible-ml explanatory-model-analysis

Updated Jun 1, 2022
Python

interpretml / DiCE

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Generate Diverse Counterfactual Explanations for any machine learning model.

machine-learning deep-learning explainable-ai explainable-ml xai interpretable-machine-learning counterfactual-explanations

Updated Jul 9, 2022
Python

csinva / imodels

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Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

python rules data-science machine-learning statistics ai scikit-learn ml artificial-intelligence supervised-learning interpretability explainable-ai explainable-ml rule-learning optimal-classification-tree rulefit imodels bayesian-rule-list

Updated Jul 6, 2022
Jupyter Notebook

EthicalML / xai

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XAI - An eXplainability toolbox for machine learning

machine-learning ai evaluation ml artificial-intelligence upsampling bias interpretability feature-importance explainable-ai explainable-ml xai imbalance downsampling explainability bias-evaluation machine-learning-explainability xai-library

Updated Oct 30, 2021
Python

lopusz / awesome-interpretable-machine-learning

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data-science machine-learning interpretable-ai interpretable-ml explainable-ai xai interpretable-machine-learning

Updated Jul 9, 2020
Python

maelfabien / Machine_Learning_Tutorials

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Code, exercises and tutorials of my personal blog ! 📝

python machine-learning statistics dataviz ai deep-learning tensorflow example keras tutorials pytorch explainable-ai

Updated Feb 18, 2020
Jupyter Notebook

wangyongjie-ntu / Awesome-explainable-AI

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A collection of research materials on explainable AI/ML

xml interpretability explanation-system interpretable-ai explainable-ai xai counterfactual-explanations recourse

Updated Jul 8, 2022

cdpierse / transformers-interpret

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Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

nlp machine-learning natural-language-processing deep-learning neural-network transformers interpretability explainable-ai captum model-explainability transformers-model

Updated Jun 18, 2022
Jupyter Notebook

microsoft / responsible-ai-toolbox

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[Error Analysis] Support for bringing in features that are not being used by the model but useful for error analysis

1

nushib commented Jun 2, 2021

Suggested by Melanie Fernandez Pradier:

“Given a model trained on certain features, is there any way I can include additional features (not used in training) but that I want to monitor in the error analysis?"

This is currently possible by enriching the set of input features to the dashboard after the inference step. However, will need further support on the UI side to clearly mark features t

documentation enhancement good first issue Error Analysis

aerdem4 / lofo-importance

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Leave One Feature Out Importance

data-science machine-learning feature-selection feature-importance explainable-ai

Updated Apr 27, 2022
Python

hila-chefer / Transformer-MM-Explainability

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[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

visualization transformers transformer vqa clip interpretability explainable-ai explainability detr lxmert visualbert

Updated Jul 9, 2022
Jupyter Notebook

BCG-Gamma / facet

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Human-explainable AI.

python data-science machine-learning statistics simulation model-selection data-analytics hyperparameter-tuning interpretability explainable-ai shap-vector-decomposition

Updated Jun 29, 2022
Jupyter Notebook

ScalaConsultants / Aspect-Based-Sentiment-Analysis

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Extract Weights and scores from result

1

AymenBenk commented Jun 29, 2021

`import numpy as np
import aspect_based_sentiment_analysis as absa
from aspect_based_sentiment_analysis import alignment
from aspect_based_sentiment_analysis import Example

text = "I love mascara"
aspects = ['mascara']

recognizer = absa.aux_models.BasicPatternRecognizer()
nlp = absa.load(pattern_recognizer=recognizer)
task = nlp(text=text, aspects=aspects)
slack = task.examples

pri

good first issue