MIT Deep Learning Basics: Introduction and Overview – Lex Fridman

Lex Fridman, a Postdoctoral Associate at the MIT AgeLab, gave a informative introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers.

The outline of this course; Deep learning, History of ideas and tools, TensorFlow and Simple example in TensorFlow, Deep learning is representation learning, Why deep learning (and why not), Challenges for supervised learning, Key low-level concepts, Higher-level methods and Toward artificial general intelligence

For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, you can visit :
Website : https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-dee…
Slides: http://bit.ly/deep-learning-basics-sl…
Playlist: http://bit.ly/deep-learning-playlist
Blog post: https://link.medium.com/TkE476jw2T

Source: Lex Fridman

Lex Fridman is a Postdoctoral Associate at the MIT AgeLab. He received his BS, MS, and PhD from Drexel University where he worked on applications of machine learning and numerical optimization techniques in a number of fields including robotics, ad hoc wireless networks, active authentication, and activity recognition. Before joining MIT, Dr. Fridman worked as a visiting researcher at Google. His research interests include machine learning, decision fusion, developing and applying deep neural networks in the context of driver state sensing, scene perception, and shared control of semi-autonomous vehicles and numerical optimization.

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