Implementing Loss and Activation Functions with JAX

Deep learning models rely on precise mathematical transformations to learn from data, yet setting up custom optimization pipelines can be a major bottleneck. This text-based course guides you through the foundational math and implementation of core neural network components using JAX, a powerful library designed for high-performance machine learning research. You will transition from conceptual formulas to clean, functional code that runs efficiently on modern hardware.\n\nBy completing this course, you will understand how activation functions shape your network's representations and how loss functions guide the optimization process. You will read through clear mathematical breakdowns, explore structured code implementations, and practice writing custom functions that leverage automatic differentiation.\n\nWhat you'll learn:\n- Understand the mathematical foundations of activation functions like ReLU, ELU, and Sigmoid\n- Implement softmax cross-entropy loss and other essential objectives from scratch\n- Apply JAX's automatic differentiation engine to compute gradients of your custom functions\n- Practice optimization patterns by writing clean, side-effect-free functional code\n- Configure network layers using modern programming practices like type hints and pure functions\n- Avoid common numerical stability issues such as log-sum-exp overflow in loss calculations\n\nThe course starts with essential definitions and mathematical concepts before walking you through step-by-step code implementations. You will build up from basic linear transformations to complete loss evaluation pipelines, gaining a deep intuition for how gradients flow through your models.\n\nThis course is designed for beginner to intermediate machine learning engineers, data scientists, and researchers who want to understand the inner workings of neural networks using JAX. No prior experience with JAX is required, though a basic familiarity with Python and linear algebra will help you get the most out of the material.\n\nStart reading today to build a deeper, more robust understanding of neural network optimization.