Generative Models and Deep Probabilistic Learning: Mathematical, Architectural, and Practical Treatise

Introduction

What this guide covers

Introduction Advanced Artificial Intelligence · Theoretical-Practical Treatise

Overview

Topics covered

• Introduction
• Generative Models and Deep Probabilistic Learning
• Mathematical Foundations and Information Theory
• Information Theory and Probability Divergences
• The Curse of Dimensionality and Maximum Likelihood
• Autoregressive Models and Explicit Density Estimation

Related concepts

Main connections

• Maximum likelihood estimation
• Neural networks & deep learning
• Linear algebra & matrices
• Normal (Gaussian) distribution
• Calculus & differentiation
• Variance & standard deviation

Compatible notation

Mathematical sample in LaTeX

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