Continuous Probability Distributions in JAX for Machine Learning

Probability is the bedrock of modern machine learning, enabling algorithms to quantify uncertainty and make predictions. This text-based course guides you through the essential concepts of continuous probability distributions, showing you how to implement them programmatically. You will start with foundational definitions, core mathematical concepts, and essential terminology before writing a single line of code. By reading through clear explanations and structured code snippets, you will gain a practical understanding of how to work with probability density functions using JAX, a high-performance numerical computing library. You will learn to manipulate key distributions and leverage modern functional programming patterns to scale your computations efficiently. What you'll learn: - Understand the mathematical foundations of continuous probability distributions and density functions - Implement normal, uniform, and exponential distributions using JAX syntax - Apply JAX's automatic differentiation and vectorization features to probabilistic models - Practice generating random samples and calculating log-likelihoods for data modeling - Configure modern functional programming patterns in JAX to write clean, reproducible code The course begins with foundational probability concepts and terminology, transitions into the core properties of continuous distributions, and concludes with hands-on JAX implementations for machine learning workflows. This program is designed for beginners in probabilistic programming, data science, and machine learning, with no advanced prerequisites other than basic Python knowledge. Start your journey into probabilistic machine learning today.