- A Case For Explainable AI & Machine Learning - Dec 27, 2018.
In support of the explainable AI cause, we present a variety of use cases covering operational needs, regulatory compliance and public trust and social acceptance.
- Machine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI - Dec 20, 2018.
We explain the key differences between explainability and interpretability and why they're so important for machine learning and AI, before taking a look at several techniques and methods for improving machine learning interpretability.
- Interpretability is crucial for trusting AI and machine learning - Nov 30, 2018.
We explain what exactly interpretability is and why it is so important, focusing on its use for data scientists, end users and regulators.
- How Important is that Machine Learning Model be Understandable? We analyze poll results - Nov 19, 2018.
About 85% of respondents said it was always or frequently important that Machine Learning model be understandable. This was is especially important for academic researchers, and surprisingly more in US/Canada than in Europe or Asia.
- New Poll: How Important is Understanding Machine Learning Models? - Oct 30, 2018.
New KDnuggets poll is asking: When building Machine Learning / Data Science models in 2018, how often was it important that the model be humanly understandable/explainable? Please vote
- Key Takeaways from the Strata San Jose 2018 - Jul 16, 2018.
By dropping 'Hadoop' from its name, the @strataconf 2018 in San Jose signaled the emphasis on machine learning, cloud, streaming and real-time applications.
- 5 Key Takeaways from Strata London 2018 - Jun 19, 2018.
5 highlights and thoughts from my attendance to Strata London 2018.
- Will GDPR Make Machine Learning Illegal? - Mar 14, 2018.
Does GDPR require Machine Learning algorithms to explain their output? Probably not, but experts disagree and there is enough ambiguity to keep lawyers busy.
- 3 principles for solving AI Dilemma: Optimization vs Explanation - Feb 14, 2018.
We propose 3 principles for maximizing the benefits of machine learning without sacrificing its intelligence.
- Top KDnuggets tweets, Dec 14-20: False positives versus false negatives: Best explanation ever - Dec 21, 2016.
Also #MachineLearning, #AI experts: Main Developments 2016, Key Trends 2017; Official code repository for #MachineLearning with #TensorFlow book; Top 10 Essential Books for the #Data Enthusiast.
- An Intuitive Explanation of Convolutional Neural Networks - Nov 11, 2016.
This article provides a easy to understand introduction to what convolutional neural networks are and how they work.
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- Introduction to Local Interpretable Model-Agnostic Explanations (LIME) - Aug 25, 2016.
Learn about LIME, a technique to explain the predictions of any machine learning classifier.
- Support Vector Machines: A Simple Explanation - Jul 7, 2016.
A no-nonsense, 30,000 foot overview of Support Vector Machines, concisely explained with some great diagrams.
- A Visual Explanation of the Back Propagation Algorithm for Neural Networks - Jun 17, 2016.
A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.
- A Simpler Explanation of Differential Privacy - Nov 6, 2015.
Privacy concerns in data mining have been raised from time to time, could differential privacy be a solution? Differential privacy was devised to facilitate secure analysis over sensitive data, learn how it can be used to improve the model fitting process.
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