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Machine Learning for Beginners (Part-II): Understanding Intermediate Concepts
- Loss function calculates the loss for one row, while cost function does so for all target data points and aggregates them over N rows.
- Mean Squared Error (MSE) aggregates squared losses over N rows, providing a better evaluation metric.
- Accuracy in supervised machine learning (ML) measures correctly predicted instances in classification, not regression.
- Derivatives in uni-variable calculus show rate of change between two close points on a curve.
- Multi-variable calculus enables understanding dependencies among input variables in derivative calculations.
- Partial derivatives in multi-variable calculus help analyze model behavior with changing input variables.
- Chain rule in calculus is essential for finding derivatives of composite functions and is vital in backpropagation in ML.
- Training algorithm encompasses the entire model training process from initialization to parameter tuning.
- Optimization algorithm focuses on minimizing the loss function for model convergence, critical for training models.
- Model training includes parametric and non-parametric methods and various training algorithms.
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