Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.
Explores stochastic models for communications, covering mean, variance, characteristic functions, inequalities, various discrete and continuous random variables, and properties of different distributions.
Explores mean, variance, probability functions, inequalities, and various types of random variables, including Binomial, Geometric, Poisson, and Gaussian distributions.
