LIRC@SFIT

An introductory lecture overviewing 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 visit our website or follow code tutorials on our GitHub repo. INFO: 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 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 Source: https://www.youtube.com/watch?v=O5xeyoRL95U (Accessed on January 22, 201...

New lecture on recent developments in deep learning is defined in the state of the art in our field (algorithms, applications, and tools). This is a complete list, but hopefully a good sampling of new exciting ideas. For more lecture videos visit our website or follow code tutorials on our GitHub repo. INFO: Website: https://deeplearning.mit.edu GitHub: https: //github.com/lexfridman/mit-dee ... Slides: http://bit.ly/2HiZyvP Playlist: http: // bit ly / deep-learning-playlist OUTLINE: 0:00 - Introduction 2:00 - BERT and Natural Language Processing 14:00 - Tesla Autopilot Hardware v2 +: Neural Networks at Scale 16:25 - AdaNet: AutoML with Ensembles 18:32 - AutoAugment: Deep RL Data Augmentation 22:53 - Training Deep Networks with Synthetic Data 24:37 - Segmentation Annotation with Polygon-RNN ++ 26:39 - DAWNBench: Training Fast and Cheap 29:06 - BigGAN: State of the Art in Image Synthesis 30:14 - Video-to-Video Synthesis 32:12 - Semantic Segmentation 36:...