Determining if results are statistically significant.
Dot products, transposition, and inversion. Mathematical Foundations of Data Science Using ...
Normal, Binomial, and Poisson patterns in data. Bayes’ Theorem: Updating beliefs based on new evidence. Determining if results are statistically significant
Updating specific weights in complex models. Chain Rule: The mathematical basis for backpropagation. 🎲 Probability & Statistics This provides the framework for making predictions. and inversion. Normal
Powering Dimensionality Reduction (PCA).
The engine behind neural network training.
SVD (Singular Value Decomposition) for compression. 📈 Calculus Calculus optimizes the models we build. Differentiation: Calculating slopes to find minima.