Capsule Networks - overcoming limitations of Convolutional Neural Networks

SWAPAN JAIN (~swapan)


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Description:

This talk will cover understanding limitations of Convolutional NN in detecting images. After understanding this limitation, I will introduce the concept of capsules. This talk is highly inspired from the paper from Geoff hinton- Dynamic routing betwen Capsules-https://arxiv.org/pdf/1710.09829.pdf I will try to explain the process of training a multi layer capsule system on MNIST and comparing it with a convolutional net at recognizing highly overlapping images. I will use Tensorflow or Keras to show my demo Jupyter notebook. I will also discuss the limitations of capsules.

Prerequisites:

1.Linear Algebra 2.Probability and Statistics 3.Any Deep Learning library (Tensorflow/pytorch/Keras) 4.Deep Learning layers- Fully connected and Convolutional layers

Content URLs:

1.Understanding Convolutional Neural Networks - CS231n Stanford-http://cs231n.stanford.edu/ 2.Any Deep Learning Library preferably Tensorflow

Speaker Info:

Hi, I am Swapan Jain. After graduating in Computer Science from Delhi Technological University, I self studied AI by reading books,papers and taking courses online. I am a prospective graduate student from fall 2019.

Speaker Links:

I currently do not have open source contributions, but I will begin the blog from August. my twitter handle is @swapanj162. I will update about my blog or any project on my twitter.

Section: Data science
Type: Talks
Target Audience: Advanced
Last Updated:

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