Understanding and Visualizing ResNets that Forever Revolutionized Deep Learning

Learn how the ResNet changed deep learning forever.

In December 2015, a published paper rocked the deep learning world. This paper is widely regarded as one of the most influential papers in modern deep learning and has been cited over 110,000 times. The name of this paper was Deep Residual Learning for Image Recognition (aka, the ResNet paper). In this session, we'll take a brief tour through the history of computer vision, into the anatomy of a convolutional neural network, and understand their limitations.

By the end of the session, you'll know:

• What computer vision was like before convolutional neural networks (CNNs)
• The anatomy of CNNs
• The limitations of CNNs
• Residual networks and the skip connection
• How to perform image classification with ResNet with code

Table of Contents:
00:00 Introduction
05:10 Typical neural network
05:48 Translational invariance
07:27 Convolutional neural network
16:45 Classic CNN architectures
25:08 Residual blocks and the skip connection
30:05 ResNet in action
52:25 QnA

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Google Colab Notebook: https://colab.research.google.com/drive/1ZqCky7dXJ8cGCFaOXCzM0ekVZ0-KPvmi?usp=sharing
Star SuperGradients on GitHub: https://github.com/Deci-AI/super-gradients
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#Deep learning #ResNet #Convolutional neural network #image recognition #neural network #convolutional neural network architectures #computer vision #image classification #object detection #image segmentation #neural networks