An 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. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.

INFO:
Website: https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-deep-learning
Slides: http://bit.ly/deep-learning-basics-slides
Playlist: http://bit.ly/deep-learning-playlist
Blog post: https://link.medium.com/TkE476jw2T

OUTLINE:
0:00 – Introduction
0:53 – Deep learning in one slide
4:55 – History of ideas and tools
9:43 – Simple example in TensorFlow
11:36 – TensorFlow in one slide
13:32 – Deep learning is representation learning
16:02 – Why deep learning (and why not)
22:00 – Challenges for supervised learning
38:27 – Key low-level concepts
46:15 – Higher-level methods
1:06:00 – Toward artificial general intelligence

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