JAX Fundamentals for Deep Learning and Probabilistic Models

Are you looking to build high-performance machine learning models with a modern, flexible framework? JAX offers a powerful foundation for scientific computing, deep learning, and probabilistic programming, enabling researchers and developers to accelerate their work. This course will equip you with the foundational knowledge and practical skills to confidently use JAX for creating and optimizing advanced machine learning models, from neural networks to complex statistical systems. What you'll learn: * Understand the core principles of JAX, including immutability and functional transformations * Learn to apply automatic differentiation and Just-In-Time (JIT) compilation for accelerated computations * Build and train neural networks using a modern JAX-based deep learning library * Explore foundational concepts and practical patterns for probabilistic programming with JAX * Apply JAX to develop custom machine learning models and efficient optimization routines This course begins by introducing JAX's unique functional programming paradigm and its core features, then progresses to building deep learning models and exploring probabilistic methods. You will learn step-by-step through clear explanations and practical code examples. This course is designed for beginners with basic Python knowledge who are interested in machine learning and want to learn JAX. No prior experience with JAX or advanced machine learning concepts is required. Start your journey with JAX and unlock new possibilities in high-performance machine learning.