IBM Webinar Deep Learning: IBM is organizing a Webinar on Introduction to Deep Reinforcement Learning on Sep 22, 4:00 PM. the detailed requirement and process of applying to the webinar given further below in the article.
So International Business Machines Corporation an American multinational technology company headquartered in Armonk, New York. It was founded in 1911 in Endicott, New York; as the Computing-Tabulating-Recording Company and was renamed “International Business Machines” in 1924.
IBM Webinar Deep Learning:
About the Webinar
Also, Reinforcement Learning (RL) is an area of Machine Learning, which deals with designing fully autonomous agents that learn by interacting with their environments. Recently deep neural network (DNN) based techniques have become popular due to their ability to automatically learn rich feature representations from data.
So, The use of DNNs within traditional reinforcement learning algorithms has accelerated progress in RL, given rising to the field of “Deep Reinforcement Learning” (DRL). Also, A few of the success stories of DRL are achieving superhuman performance on “Atari Games” by just using the image pixels, beating the human world champion in the game of “Go”. In this talk, we will provide a gentle introduction to DRL and show how to train agents in OpenAI Gym environments.
About the Speaker
Dinesh Khandelwal is a Research Scientist at IBM India Research Lab. He has a Ph.D. in Machine Learning from IIT Delhi. His primary research interests lie in the area of Deep Learning, Probabilistic Graphical Models, and Question Answering. He also holds a Master’s degree in Machine Learning from IISc Bangalore. He has published in top venues for Computer Vision, Machine Learning, and NLP, including AAAI, ACL, CVIU, WACV, and PRL.
How to Register?
Interested participants can register for the webinar through furthe below link
To Apply: this link.
Sep 21, 2020
For more details, click the link below.
Webinar on Introduction to Deep Reinforcement Learning by IBM
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1 thought on “IBM Webinar on Introduction to Deep Reinforcement Learning”
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