AML S1E5 Loss Functions | Applied Machine Learning

In this episode of the Machine Learning Series, we unravel the importance of loss functions in machine learning, covering Cross-Entropy (CE) for multi-class classification, Binary Cross-Entropy (BCE) for binary tasks, Mean Squared Error (MSE) for regression, and Mean Absolute Error (MAE) as a robust alternative to MSE. Through hands-on examples using true labels (y) and predictions (y^​), we demonstrate how these loss functions work, their mathematical underpinnings, and scenarios where each is most effective. With tips for logarithmic simplifications, a comparison of CE, BCE, MSE, and MAE, and GATE-style practice questions, this episode is packed with insights for students and ML enthusiasts alike!
#ai #machinelearning #loss #machinelearningplus #artificialintelligence #deeplearning
Notes Link : github.com/Esk...