Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. taken from https://cheeze.club/msre 9th Aug 2017, 5:05 AM David Sebastian Keshvi Illiakis + 1 https://www.instagram.com/about/jobs/...
For instance, as mentioned, machine learning is all about training an algorithm. But, to go further, in order to train an algorithm, you need a neural network—which is a set of algorithms inspired by biological neural networks. To connect this neural network to something they know, explain ...
What’s particularly interesting about machine learning is that it gets more and more powerful as it gets access to more and more data. It’s a bit like the opposite of diminishing returns, an impressive snowball effect that acts as a gift that keeps on giving. Machine learning, then, unde...
Do you ever need to explain something to others unfamiliar with your work what it's about? One situation I frequently face is explaining machine learning to audiences who want to learn more about it but are not yet particularly conversant in it. This is regardless of the audi...
ExplainX.ai is a fast, scalable and end-to-end Explainable AI framework for data scientists & machine learning engineers. Understand overall model behavior, get the reasoning behind model predictions, remove biases and create convincing explanations for your business stakeholders with explainX. ...
DeepLIFT (Deep Learning Important Features) Layer-wise relevance propagation 4 增加复杂性以解决复杂性:它能提高透明度吗 参考文献 1 引言 尽管Interpretability和Explainability都被翻译成“可解释性”,但在机器学习领域,这是两个相关但又截然不同的概念。
Machine Learning Explanations as Boundary Objects: How AI Researchers Explain and Non-Experts Perceive Machine LearningAmid AyobiKatarzyna StawarzDmitri S. KatzPaul MarshallTaku YamagataRaúl Santos-RodríguezPeter A. FlachAisling Ann O'KaneCEUR Workshop ProceedingsWorkshop on Transparency and Explanations ...
Also, could one of the very important features be a potential data leak? All these questions allow us to improve the model before deploying a more responsible and robust machine-learning model. Note:If you are interested in learning more about responsible AI, I have also written a piece on ...
About A game theoretic approach to explain the output of any machine learning model. shap.readthedocs.io Topics machine-learningdeep-learninggradient-boostinginterpretabilityshapleyshapexplainability Resources Readme License MIT license Activity Custom properties ...
For data science teams to succeed, business leaders need to understand the importance of MLops, modelops, and the machine learning life cycle. Try these analogies and examples to cut through the jargon.