Artificial intelligence has revolutionized how we interact with the world, from personal assistants to self-driving cars. Deep neural networks, in particular, have driven much of this progress. However, these networks are typically large, complex, and computationally expensive. In some cases, it is not feasible to use these models in real-world applications, especially when deploying to low-powered devices. To solve this problem, researchers have developed a technique known as knowledge distillation , which allows us to compress large neural networks into smaller, faster, and more efficient ones. In this blog post, we will explore the concept of knowledge distillation, its mathematical underpinnings, and its applications. Additionally, we will provide an implementation of knowledge distillation in Keras, one of the most popular deep-learning frameworks. https://neptune.ai/blog/knowledge-distillation What is Knowledge Distillation? Knowledge distillation is a technique used to transf