Week 1 – Lecture: History, motivation, and evolution of Deep Learning

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Course website: http://bit.ly/pDL-home
Playlist: http://bit.ly/pDL-YouTube
Week 1: http://bit.ly/pDL-en-01

0:00:00 – Week 1 – Lecture

LECTURE Part A: http://bit.ly/pDL-en-01-1
We discuss the motivation behind deep learning. We begin with the history and inspiration of deep learning. Then we discuss the history of pattern recognition and introduce gradient descent and its computation by backpropagation. Finally, we discuss the hierarchical representation of the visual cortex.
0:03:37 – Inspiration of Deep Learning and Its History, Supervised Learning
0:24:21 – History of Pattern Recognition and Introduction to Gradient Descent
0:38:56 – Computing Gradients by Backpropagation, Hierarchical Representation of the Visual Cortex

LECTURE Part B: http://bit.ly/pDL-en-01-2
We first discuss the evolution of CNNs, from Fukushima to LeCun to Alexnet. We then discuss some applications of CNN's, such as image segmentation, autonomous vehicles, and medical image analysis. We discuss the hierarchical nature of deep networks and the attributes of deep networks that make them advantageous. We conclude with a discussion of generating and learning features/representations.
0:49:25 – Evolution of CNNs
1:05:55 – Deep Learning & Feature Extraction
1:19:27 – Learning Representations

YouTube url:	https://www.youtube.com/watch?v=0bMe_vCZo30
Created:	25. 9. 2020 14:06:15

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Week 1 – Lecture: History, motivation, and evolution of Deep Learning