Most respected Machine Learning researchers and their contributions

Machine learning is only possible with all great minds. You should read the works of the top experts in machine learning if you want to learn more and widen your views. Learning from the brightest minds in science is fascinating. Many different types of influential people, educators, and business owners are among them. Their knowledge has the potential to improve the planet.

Here is a list of all the people your machine learning research career needs you to know. Gain knowledge and be motivated to seek out new information.

Mr. Geoffrey Hinton

Who else would be at the top of a list generated by machine learning? Emeritus Professor Geoffrey Hinton is now a researcher at Google Brain and employed by the University of Toronto.

He gained most of his notoriety via his work with artificial neural networks (ANNs). He is often referred to as the "Godfather of Deep Learning," which is a legitimate title. He deserves to be regarded as a genius since he contributed significantly to developing deep learning.

His work on the backpropagation method dramatically enhanced deep learning models. Mr. Hinton's work with Boltzmann machines and capsule neural networks has significantly advanced the field. Both represent important developments in our field.

Mr. Geoffrey Hinton earned the IEEE/RSE Wolfson James Clerk Maxwell Award and the BBVA Foundation Frontiers of Knowledge Prize.

Michael Jordan

Professor Michael Jordan teaches at the University of California, Berkeley. Deep learning, statistics, and machine learning are some of his areas of study interest. He has contributed importantly to natural language processing (NLP), probabilistic graphical models, spectral techniques, and other areas, and his aid with Bayesian networks.

He has won several awards, including the ACM-AAI Allen Newell Award, the IEEE Neural Networks Pioneer Prize, and the American Control Conference (ACC) best paper prize, which he shared with R. Jacobs. He has also received honors from the Institute of Mathematical Statistics, including Neyman and Medallion Lectureships.

The talk he gave at SysML, which is online, should be seen by everyone with even a passing interest in machine learning. It summarises the subject well and helps put the current hysteria in perspective.

Andrew Ng

Andrew Ng may be the name most associated with machine learning among people interested in it. He is recognized among deep learning and machine learning experts as one of the most important figures in the field.

Andrew Ng's current position is Adjunct Professor in the Department of Computer Science at Stanford University, which he co-founded with deeplearning.ai and Coursera. Professor Andrew helped to create the Google Brain project while serving as the Chief Scientist of Baidu before joining Google.

His goals are to increase interest in deep learning and to make education available to everyone on the earth for free. He provides the best and most well-liked online deep learning and machine learning training.

Yann LeCun

Yann LeCun is another well-known figure in the field of machine learning. He has managed R&D, performed research, and lectured. Yann LeCun has worked in both academic and commercial contexts, making him a specialist in machine learning, deep learning, computer vision, and robotics.

Facebook's Vice President and Chief AI Scientist is Mr. LeCun.

Yann LeCun could have been the first to consider convolutional neural networks. Applications for computer vision often use a method called backpropagation, and Convolutional neural networks were able to use it because of its success. This is only the beginning of the help you may get from this educator.

More than 150 works have been published, and he has received several honors for his accomplishments. He has received the PAMI Distinguished Researcher Award and the IEEE Neural Network Pioneer Award. He was given the IEEE award in 2014 and the PAMI award in 2015. In 2018, LeCun was one of three Turing Award recipients; the other two were Geoffrey Hinton and Yoshua Bengio.

Yoshua Bengio

Yoshua Bengio is now a professor at the University of Montreal, employed with the Department of Computer Science and Operations Research. Additionally, he contributed to establishing Element AI, a Montreal-based company whose mission is to make advances in artificial intelligence available to everybody.

Yoshua became well-known for his work on deep learning and artificial neural networks (1980s and 1990s). Along with Yann LeCun, he was a major force behind the controversial ICLR summit. He is widely regarded as the top expert in the world in natural language, Bayesian learning, recurrent networks, and deep learning.

Yoshua's contributions to deep learning are more diverse than his peers, making them stand out. He probably has yet to study every facet of deep learning, but that is unlikely.

The Turing Prize and the Canada Research Chair in Statistical Learning Algorithms were both given to him in 2018. For him, both of these ideas had favorable effects.

Jurgen Schmidhuber

It would help if you studied more about Jurgen Schmidhuber. He is a well-known computer specialist who has contributed to deep learning and artificial neural networks.

His ultimate objective is to create artificial intelligence that can learn and grow on its own until it becomes smarter than it is. He assisted in developing long short-term memory (LSTM), a kind of recurrent neural network, with a couple of his students.

He accelerated convolutional neural networks using graphics processing units (GPUs). Over 300 of Mr. Schmidhuber's articles have undergone peer review, and he has garnered several awards.

He serves as the Chief Scientist of the company NNAISENSE, whose goal is to develop the first genuine general-purpose AI. He aids several administrations in creating AI strategies.

Conclusion

Machine learning is an effective method that might significantly improve society. You may expand your knowledge and perspectives by reading the writings of prominent experts in machine learning. The deep learning and machine learning divisions are progressing more quickly, so staying on top of developments in your field is important.