Human and Machine Learning : Visible, Explainable, Trustworthy and Transparent

Human and Machine Learning: Visible, Exp

📁 Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent

✍ Jianlong Zhou, Fang Chen 📂 Library 📅 2018 🏛 Springer 🌐 English

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be int

Practicing Trustworthy Machine Learning:

📁 Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines

✍ Yada Pruksachatkun, Matthew Mcateer, Subhabrata Majumdar 📂 Library 📅 2023 🏛 O'Reilly Media 🌐 English

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help developmen

Practicing Trustworthy Machine Learning:

📁 Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines

✍ Yada Pruksachatkun, Matthew Mcateer, Subhabrata Majumdar 📂 Library 🏛 O'Reilly Media 🌐 English

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help d

Practicing Trustworthy Machine Learning:

📁 Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines

✍ Yada Pruksachatkun, Matthew Mcateer, Subhabrata Majumdar 📂 Library 🏛 O'Reilly Media 🌐 English

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help d

Applied Machine Learning Explainability

📁 Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

✍ Aditya Bhattacharya 📂 Library 📅 2022 🏛 Packt Publishing 🌐 English

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems Key Features <ul><li>Explore various explainability me

Applied Machine Learning Explainability

📁 Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

✍ Aditya Bhattacharya 📂 Library 📅 2022 🏛 Packt Publishing 🌐 English

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems Key Features <ul><li>Explore various explainability me