In this episode of the SuperDataScience Podcast, I chat with the Machine Learning Research Scientist, John Langford. You will hear about unsupervised, supervised learning and reinforcement learning, and the differences between the three. You will learn about applications of contextual bandits and reinforcement learning in general, YOLO style algorithms versus simulator algorithms, technics for avoiding local optimums. You will also learn about the balance between exploration and exploitation, learning to search and active learning.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/275

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 275: Machine Learning Through Reinforcement & Contextual Bandits

พูดคุยแบ่งปันเกี่ยวกับวิธีรื้อค้นและสร้างตัวตนขึ้นมาใหม่ (reinvent) ให้พร้อมสำหรับการทำงานในโลกอนาคต (ที่มี tech มาเกี่ยวข้อง) โดย เธมส์ THINKต่าง ผู้เขียนหนังสือ THINKต่าง 78 ความคิดใหม่เพื่อสำเร็จไวกว่าเดิม และ เปลี่ยนงานประจำธรรมดาเป็นวิชาสร้างชีวิต  | fb page : thinkต่าง by เธมส์ และ 2050 |
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The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

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SDS 275: Machine Learning Through Reinforcement & Contextual Bandits

Super Data Science: ML & AI Podcast with Jon Krohn