Inductive Biases in Machine Learning: Understanding their Impact on AI Predictions скачать в хорошем качестве

Inductive Biases in Machine Learning: Understanding their Impact on AI Predictions

Inductive Biases in Machine Learning: Understanding their Impact on AI Predictions 💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇 👉 https://xbe.at/index.php?filename=Ind... Inductive biases are the presuppositions and assumptions that artificial intelligence (AI) models bring to new problems, based on their prior experiences or design. Understanding these biases is crucial for accurately interpreting AI predictions and ensuring fairness and transparency in machine learning models. First, let's explore the concept of inductive learning. In this approach, machines learn from observed examples, rather than deductively applying rules and logic. Inductive biases provide a foundation for the learning process, helping to guide the model's generalization capabilities. There are various sources of inductive biases in machine learning models. Explicit biases are introduced through the design and architecture of the model, such as using a particular neural network architecture or hand-engineered features. Implicit biases, on the other hand, are learned from the training data through statistical patterns and frequency distribution. Ongoing research in the field aims to identify and mitigate bias in AI models to promote fairness and accuracy. The Adversarial Debiasing and Correction workshop series and the Fairlearn library are two notable efforts in this area. Additional Resources: -estlyean.github.io/PublicationProposalHisamMangal/P yardD2019.pdf [Understanding and Mitigating Inductive Bias in Machine Learning] -fairlearn.org [Fairlearn: A Library for Studying and Mitigating Statistical Biases in Machine Learning] -arxiv.org/abs/2101.06056 [A Survey of Inductive Bias in Machine Learning: From Theory to Practice] #STEM #MachineLearning #InductiveBias #AI #ArtificialIntelligence #Technology # Find this and all other slideshows for free on our website: https://xbe.at/index.php?filename=Ind...