ResNet Image Classification with Flax and JAX

High-performance deep learning requires tools that can scale seamlessly across modern hardware. JAX and Flax offer a powerful, functional-first approach to building neural networks with incredible speed and flexibility. In this course, you will transition from understanding the fundamental mathematical concepts of residual networks to writing clean, optimized Flax code. You will learn how to structure deep learning pipelines, utilize JAX's powerful transformations, and apply state-of-the-art architectures to real-world image classification tasks. What you'll learn: Understand the core architecture of Residual Networks (ResNet) and how skip connections solve the vanishing gradient problem; Build custom ResNet layers and blocks using the functional Flax Linen API; Apply JAX transformations like jit, grad, and vmap to optimize training speed and execution; Implement training loops with robust state management and optimizer pipelines using Optax; Fine-tune pre-trained ResNet models for custom image classification tasks using modern transfer learning workflows; Save, load, and manage model checkpoints efficiently for future deployment. You will start with the essential theory of residual learning before diving into step-by-step code implementations of ResNet blocks. The text-based material guides you through setting up data pipelines, executing training loops, and scaling your models with functional transformations. This course is designed for developers and data enthusiasts who are new to JAX and Flax, requiring only a basic understanding of Python and general machine learning concepts. Start reading today to master high-performance deep learning with Flax and JAX.