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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. These algorithms use existing data like text, images, and audio to generate content that looks like it comes from the real world.

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New machine learning program accurately predicts who will stick with their exercise program

Flipboard

This study, published in the journal, Scientific Reports , looked at data collected between 2009 and 2018 from the National Health and Nutrition Examination Survey, a large U.S. Using six different machine learning algorithms, the researchers built 18 prediction models to test various combinations of factors. According to U.S.

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Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches

Flipboard

A 130-line F8-F9 barley population caused Badia and Kavir to grow at the Gonbad Kavous University Research Farm on three planting dates (19 November, 19 January, and 19 March), with three replicates in 2018/2019 and 2019/2020. The Bayesian algorithm was utilized to optimize the parameters of the machine-learning models.

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Are AI technologies ready for the real world?

Dataconomy

AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand. Common choices include neural networks (used in deep learning), decision trees, support vector machines, and more. Over time, the algorithm improves its accuracy and can make better predictions on new, unseen data.

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Explainability in AI and Machine Learning Systems: An Overview

Heartbeat

Algorithmic Accountability: Explainability ensures accountability in machine learning and AI systems. It allows developers, auditors, and regulators to examine the decision-making processes of the models, identify potential biases or errors, and assess their compliance with ethical guidelines and legal requirements. Russell, C. &

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What a data scientist should know about machine learning kernels?

Mlearning.ai

Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data. When and where each kernel is used?

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How To Improve Machine Learning Model Accuracy

DagsHub

In 2018, there were extensive news reports that an Uber self-driving car made an accident with a pedestrian in Tempe, Arizona. Image Credits: The New York Times Read more: [link] In another 2018 story , Amazon was found to show bias toward male candidates in the recruitment process because of an issue with their AI-powered HR recruiting tool.