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University of Central Florida

How Can I Explain This to You? An Empirical Study of Deep Neural Net Explanation Methods - Spring 2021

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Empirical study comparing deep neural network explanation methods, examining usability, stability, and privacy risks across various visual explanation frameworks.
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25 Lesons
37 minutes
On-Demand
Free-Video
1littlecoder

SHAP and Shapley Values for Interpretable Machine Learning - Kaggle 30 Days of ML Day 18

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Explore SHAP and Shapley Values for interpretable machine learning. Learn local interpretation techniques, Python implementation, and visualization methods for explainable AI.
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1 Lesons
21 minutes
On-Demand
Free-Video
1littlecoder

What is Interpretable Machine Learning - ML Explainability - with Python LIME Shap Tutorial

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Explore interpretable machine learning with Python, covering LIME and SHAP techniques for explainable AI. Learn why ML explainability matters and how to implement it in your projects.
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8 Lesons
41 minutes
On-Demand
Free-Video
1littlecoder

Understanding SHAP Summary Plot - Interpretable Machine Learning - Day 19

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Explore SHAP and Shapley values for interpretable machine learning, focusing on summary plots and dependence plots to enhance model explainability and insights.
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1 Lesons
21 minutes
On-Demand
Free-Video
Linux Foundation

Mitigating Bias in Models with SHAP and Fairlearn

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Learn techniques to identify and mitigate bias in machine learning models using SHAP and Fairlearn, enhancing fairness and transparency in AI systems.
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1 Lesons
42 minutes
On-Demand
Free-Video
Computational Genomics Summer Institute CGSI

Interpretability and Out-of-Distribution Generalization in Deep Predictive Models

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Explore interpretability and out-of-distribution generalization in deep predictive models, focusing on nuisance-induced spurious correlations and real-time Shapley value estimation techniques.
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1 Lesons
41 minutes
On-Demand
Free-Video
Association for Computing Machinery (ACM)

Probing ML Models for Fairness with the What-if Tool and SHAP

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Explore ML model fairness using What-if Tool and SHAP. Learn to analyze and visualize model behavior, detect biases, and understand feature importance for responsible AI development.
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1 Lesons
56 minutes
On-Demand
Free-Video
MLCon | Machine Learning Conference

Data to the Rescue! Predicting and Preventing Accidents at Sea

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Predicting and preventing maritime accidents using machine learning, leveraging ship behavior data, weather information, and operational profiles to revolutionize the marine insurance industry.
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41 Lesons
30 minutes
On-Demand
Free-Video
MLCon | Machine Learning Conference

Data to the Rescue - Predicting and Preventing Accidents at Sea

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Revolutionizing marine safety through data science: predicting accidents using ship behavior, location, speed, and weather data. Challenges of introducing ML to traditional industry and explaining models with SHAP.
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1 Lesons
24 minutes
On-Demand
Free-Video
MLCon | Machine Learning Conference

First Steps to Interpretable Machine Learning

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Explore interpretable machine learning using SHAP values to understand model predictions and feature importance, based on a real data science project with practical insights.
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15 Lesons
21 minutes
On-Demand
Free-Video
Data Science Festival

Retail Media Science: From Measurement to Optimization

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Explore retail media fundamentals, budget optimization techniques, and advanced marketing modeling approaches for effective measurement and optimization in the retail media landscape.
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1 Lesons
52 minutes
On-Demand
Free-Video
Data Council

How to Interpret and Explain Black Box Models

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Explore popular model explanation techniques for machine learning interpretability, including LIME, SHAP, and visual analytics. Gain insights into making complex models more understandable and explainable.
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1 Lesons
31 minutes
On-Demand
Free-Video