Explore the world of CycleGANs in this comprehensive lecture by child prodigy Tanishq Abraham. Delve into the intricacies of this novel deep learning tool and its applications in pathology. Learn about unpaired image conversion problems, model architecture, and training techniques. Discover the potential and limitations of CycleGANs through code demonstrations and real-world examples in pathology and microscopy. Gain insights into generator and discriminator architectures, loss functions, and implementation using popular deep learning frameworks. Explore practical aspects such as learning rate scheduling, inference, and creating web interfaces for CycleGAN models. Understand the challenges and failures associated with CycleGANs, particularly in the context of pathology applications.