Deep learning is a transformative tool in modern data analysis, yet many statisticians struggle to find a practical entry point into this powerful field. Traditional approaches, such as learning through image classification with Convolutional Neural Networks (CNNs), often fail to bridge the gap between theoretical understanding and real-world applications. This seminar takes a fresh, tailored approach to equip statisticians with the foundational knowledge and practical skills needed to leverage deep learning in their day-to-day work.

Starting with Python-based implementation of linear models, we deconstruct the core mechanisms of deep learning, including backpropagation and gradient-based optimization. By building on these principles, participants will learn to use computational graphs to construct and train more complex models. This methodology not only clarifies how deep learning works but also reveals its applicability to a wide range of real-world problems. Join us to discover the most efficient and intuitive pathway for statisticians to master the tools of artificial intelligence.