Neural Networks

Deep Learning Notes - 3, covering classification models (discriminant functions, logistic regression, softmax) and deep neural networks (activation functions, transfer learning, mixture density networks). Corresponding to Chapters 5-6 of "Deep Learning: Fundamentals and Concepts".

Deep Learning Notes-1, covering overfitting and underfitting, regularization, probability theory fundamentals (Bayes' theorem, Gaussian distribution, maximum likelihood estimation), and information theory (entropy and KL divergence). Corresponds to Chapters 1-2 of "Deep Learning: Foundations and Concepts".