Mathematical Foundations of Deep Learning

Common mathematical concepts used in deep learning include: Linear Algebra: Vectors and Matrices:

Fundamental for data representation and transformations. Matrix Multiplication: Used in neural network

operations to combine inputs and weights. Calculus: Derivatives: Essential for understanding

how to optimize loss functions using gradient descent. Partial Derivatives: Used in backpropagation

to compute gradients for each weight in the network. Probability and Statistics:

Probability Distributions: Important for understanding data and model predictions (e.g., Gaussian distribution).

Bayesian Inference: Used in probabilistic models and understanding uncertainty in predictions. Optimization:

Gradient Descent: A method for minimizing loss functions by iteratively updating model parameters.