Eric Zelikman is a deep learning engineer with a keen interest in how algorithms can learn meaningful representations. A recent graduate of Stanford University's Symbolic Systems program, he focuses on developing efficient, robust, and disentangled representations within the field of machine learning. Zelikman believes that insights from machine learning can inform broader human challenges, aiming to leverage this technology to address significant global issues. He is currently engaged with DeepLearning.AI and has contributed to various research initiatives that explore the intersections of language models and reasoning.

Sharon Zhou is a distinguished AI researcher and entrepreneur who uniquely combines classical education with cutting-edge technology. As co-founder and CEO of Lamini, she develops innovative LLM solutions while maintaining her role as an influential educator through DeepLearning.AI. A Stanford Ph.D. graduate advised by Andrew Ng, she created one of Coursera's largest classes on Generative Adversarial Networks, reaching over 250,000 students. Zhou holds the distinction of being the first to major in both Classics and Computer Science at Harvard University, graduating summa cum laude in both fields. Named in MIT Technology Review's 35 Under 35, she serves as an AI advisor to Washington D.C. policymakers while advancing her mission of making AI technology accessible to everyone. Her research spans theoretical and applied AI in medicine, climate, and social good, building on her experience as a former machine learning product manager at Google

Eda Zhou is a dedicated Curriculum Developer at DeepLearning.AI, where she focuses on advancing education in artificial intelligence and machine learning. She holds both Bachelor’s and Master’s degrees in Computer Science from Worcester Polytechnic Institute, with a concentration in cybersecurity. Eda is passionate about leveraging new technologies to enhance user protection, particularly in the realm of computer networks. Her academic background and interest in AI drive her to explore innovative applications of cutting-edge techniques in security.At DeepLearning.AI, Eda has developed several online courses centered around Generative Adversarial Networks (GANs), including "Apply Generative Adversarial Networks (GANs)," "Build Basic Generative Adversarial Networks (GANs)," and "Build Better Generative Adversarial Networks (GANs)." These courses are designed to provide learners with practical skills and knowledge necessary to implement GANs effectively. Eda's commitment to education is evident in her focus on making complex concepts accessible and engaging, empowering students to harness the potential of AI technologies in various applications. Through her work, she continues to contribute significantly to the field of AI education.