Explores Stochastic Differential Equations with examples like Brownian Motion and Square-Root Processes, discussing their relation to Partial Differential Equations.
Explores Hausdorff dimension and its application to Brownian motion sets, emphasizing the importance of understanding set dimensions in stochastic processes.
Explores the history, models, training, convergence, and limitations of neural networks, including the backpropagation algorithm and universal approximation.