Unveiling the Mystery of Deep Learning: Past, Present, and Future

Deep learning has revolutionized artificial intelligence, but its journey from early theoretical foundations to modern breakthroughs has been long and complex.

This lecture series explores the historical evolution of deep learning, tracing its origins from the early days of neural networks in the 1980s to its resurgence in the 2010s and 2020s. We will examine why deep learning remained dormant for decades—limited by computational constraints, lack of large datasets, and inefficient training methods—and how advances in hardware (GPUs), data availability, and algorithmic improvements triggered its rise.

Rather than merely introducing popular tools and frameworks, this course takes a comprehensive and insightful approach to understanding the progression of deep learning. By analyzing key milestones, from early perceptrons and backpropagation to LSTMs, CNNs, and Transformers, we aim to provide a structured perspective on how deep learning has evolved and where it is headed.

This journey will equip participants with a deeper intuition of AI’s development, enabling them to think critically about future innovations rather than just follow trends. More importantly, by understanding the strengths and limitations of different techniques across time, participants will be better equipped to choose the most suitable approach for their specific problems and data.

This lecture series will be hosted by RCAC in conjunction with Purdue’s Institute for Physical AI (IPAI).