Deep Learning Fundamentals using the JAX Framework

Deep learning requires tools that offer both flexibility for experimentation and high performance for training large models. The JAX framework, built on functional programming principles and automatic differentiation, provides a cutting-edge solution for modern numerical computing. This course guides you through mastering the core concepts of deep learning while gaining practical skill in JAX, enabling you to implement foundational neural network algorithms efficiently and prepare them for scaling. What you'll learn: * Understand the mathematical and algorithmic foundations of neural networks and backpropagation. * Apply JAX's core functional primitives, including automatic differentiation (`grad`) and vectorization (`vmap`). * Implement foundational deep learning components, such as linear layers and activation functions, from scratch. * Configure and train models using modern optimization algorithms and standard loss functions. * Practice developing robust, immutable training loops using the pure functional paradigm required by JAX. * Learn how JAX uses XLA compilation to accelerate numerical computations on various hardware. The course begins by establishing the necessary mathematical context and introducing the JAX programming model. We then progress to building and training standard multilayer perceptrons and exploring techniques for optimization and debugging. This course is designed for beginners in deep learning who have a basic working knowledge of Python and NumPy. No prior experience with JAX or other deep learning frameworks is required. Start your journey toward building high-performance machine learning models today.