Dr. Andrew Ng: A co-founder of Google Brain and former Chief Scientist at Baidu, Dr. Ng is a recognized figure in deep learning. His online courses on Coursera have educated millions about machine learning, deep learning, and AI.
Dr. Yann LeCun: Serving as the Chief AI Scientist at Facebook, LeCun’s foundational work on convolutional neural networks (CNN) has been instrumental for the advancements in computer vision. In 2018, he was a recipient of the Turing Award for his work in deep learning.
Dr. Geoffrey Hinton: Another Turing Award recipient alongside LeCun, Hinton is a Vice President and Engineering Fellow at Google. His research on neural networks and backpropagation algorithms has been seminal in the field of deep learning.
Dr. Fei-Fei Li: The co-director of Stanford University’s Human-Centered AI Institute and the co-founder of AI4ALL, Dr. Li is recognized for her work in developing ImageNet, a massive visual database crucial for recent advancements in computer vision.
Dr. Yoshua Bengio: Completing the trio of Turing Award recipients for deep learning, Bengio’s foundational work on artificial neural networks, particularly in the formulation of generative adversarial networks (GANs), has made significant contributions to the ML community.
Dr. Ian Goodfellow: Widely recognized for introducing Generative Adversarial Networks (GANs), Goodfellow’s research has paved the way for new ML techniques in image generation, style transfer, and more.
Jeremy Howard: Founder of fast.ai, Howard has democratized ML education. His deep learning courses, alongside the fastai library, have simplified complex ML concepts, allowing more people to enter the field.
Dr. Daphne Koller: A co-founder of Coursera and a leading figure in the application of ML in biomedicine, Koller’s work includes probabilistic graphical models and their application in genetics.
Dr. Sebastian Thrun: As the founder of Google X and Udacity, Thrun’s work spans a vast array of ML applications, including self-driving cars and augmented reality. His online courses have been instrumental in disseminating ML knowledge.
Dr. Sara Hooker: A Google Brain researcher, Hooker’s work often dives deep into the interpretability and robustness of machine learning models, emphasizing the importance of understanding and trusting the predictions made by ML systems.
