Understanding and Visualizing ResNets that Forever Revolutionized Deep Learning | Machine 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 -- Google Colab Notebook: https://colab.research.google.com/drive/1ZqCky7dXJ8cGCFaOXCzM0ekVZ0-KPvmi?usp=sharing Star SuperGradients on GitHub: https://github.com/Deci-AI/super-gradients -- For more captivating community talks featuring renowned speakers, check out this playlist: https://youtube.com/playlist?list=PL8eNk_zTBST-EBv2LDSW9Wx_V4Gy5OPFT For further tutorials on advanced machine learning, check out this exclusive playlist: https://youtube.com/playlist?list=PL8eNk_zTBST_SS_czCz6Do1yrUowhKBHI -- At Data Science Dojo, we believe data science is for everyone. Our data science trainings have been attended by more than 10,000 employees from over 2,500 companies globally, including many leaders in tech like Microsoft, Google, and Facebook. For more information please visit: https://hubs.la/Q01Z-13k0 💼 Learn to build LLM-powered apps in just 40 hours with our Large Language Models bootcamp: https://hubs.la/Q01ZZGL-0 💼 Get started in the world of