Groups
Numerical differentiation uses finite differences to estimate derivatives when an analytical derivative is hard or impossible to obtain.
Stochastic Gradient Descent (SGD) updates model parameters using small random subsets (mini-batches) of data, making learning faster and more memory-efficient.