This talk on Introduction to JAX and FLAX aimed to simplify complex concepts and provide the audience with a clear understanding of these powerful libraries. I started by explaining JAX's significance in automatic differentiation and accelerated linear algebra for improved numerical computations and machine learning tasks. Moving on to FLAX, I highlighted its user-friendly abstractions for building neural networks effortlessly. Practical examples demonstrated how JAX and FLAX enable efficient gradient computations, making model development and training seamless. From basic mathematical operations to real-world applications like image recognition and natural language processing, the audience witnessed the versatility of these libraries. I concluded by sharing real-world use cases, igniting curiosity among listeners to explore the potential of JAX and FLAX further in their machine learning projects.