Eric Zelikman

Eric Zelikman is a passionate Deep Learning Engineer and Curriculum Engineer at DeepLearning.AI. With a strong academic background and a fascination for machine learning and representation learning, Eric is dedicated to exploring how algorithms can learn meaningful, efficient, and robust representations. He holds a degree in Symbolic Systems from Stanford University, where he focused on the intersections of machine learning, artificial intelligence, and cognitive science. His research interests revolve around disentangled representations in machine learning models and how these can be used across various fields of AI to build more interpretable and effective systems.Eric’s work is centered on the belief that machine learning algorithms can provide insights not only for improving AI systems but also for understanding more about human cognition and real-world challenges. His ultimate goal is to leverage AI and ML techniques to address some of the most pressing issues facing humanity today. As a Curriculum Engineer at DeepLearning.AI, Eric contributes to the creation of cutting-edge educational content that helps students learn and implement Generative Adversarial Networks (GANs), one of the most powerful tools in modern deep learning.