Deep Learning is often hidden behind a wall of complex calculus and years of academic study. But for the modern Python developer, the barrier to entry has never been lower. This talk is a fast-paced, "code-first" journey into the heart of computer vision. Using the legendary MNIST dataset—a challenge that once occupied PhDs for decades—we will demonstrate how to architect, train, and optimize a model using tf.keras. Key Takeaways: Foundation: Demystifying what a neural network actually is (it's just Python!). Evolution: Moving from a simple 1-layer model to a powerful Convolutional Neural Network (CNN). The Pro Kit: Implementing "tools of the trade" like Learning Rate Scheduling, Dropout, and Batch Normalization to boost performance. The Reality Check: Identifying and defeating overfitting to ensure your model works in the real world. By the end of this session, you’ll see that 100 lines of Python is all it takes to solve one of the most famous problems in computer science history.