Covers: implementation of Convolutional Neural Network
Estimated time needed to finish: 30 minutes
##### Questions this item addresses:
• How to implement convnets in PyTorch?
##### How to use this item?

This sub-repo has 2 Notebooks. One of the main concepts for Convolutional-Networks and the other is a Challenge that contains all the structure to build your Neural Network from scratch! The recommended use of this is:

1.- Take your time to solve the notebook3-Convolutions.

2.- Contrast your notebook with their respective Notebook’s in ./Solutions File.

3.- Go to the Code-Challenge notebook and try to build your NN from scratch.

In this notebook, you will learn about:

• Convolutions
• Smoothing/Binning functions: Hann Kernel
• Smoothing/Binning cont.
• 2D Convolutions
• Pooling
• Dilated Convolutions
• Edge detection operators: Sobel & Scharr
• ConvNets
• DropOut
##### Author(s) / creator(s) / reference(s)
Amir Hajian
Programming Languages: Python
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### Extracting Features Using Convolution And Regularization

Contributors
Total time needed: ~4 hours
Objectives
Feature extraction via convolution kernels
Potential Use Cases
Building a DL architecture for for computer vision model.
Who is This For ?
INTERMEDIATE
Click on each of the following annotated items to see details.
Resources6/7
VIDEO 1. Mathematics of Convolution
• Why wee need more than MLP?
• What is a convolution and kernel?
• What is a Hann function and how to apply it?
• What are ConvNets and what is their architecture?
31 minutes
VIDEO 2. Why Convolutions? Sobel & Scharr Filters
• Why to use Convolutions?
• What is an image filter?
• What is image segmentation and how is it done?
• What convolution does really do?
• What are two famous and edge detection filters / algorithms?
16 minutes
VIDEO 3. 2D Convolutions, Pooling, and Dilated Convolutions
• What is pooling and padding?
• What other 2D convolution techniques do exist?
• When do we use padding and how?
• When do we use pooling and how?
• What are dilated convolutions and how they differ from standard convolutions?
30 minutes
VIDEO 4. Conv-Nets
• How can I apply Sobel and Scharr Operator on image?
• What is the difference between Sobel and Scharr Operator and how can I visually compare them?
• What is a Convolutional Neural Network (CNN)?
• How can one understand a CNN?
20 minutes
VIDEO 5. Regularization using Dropouts
• Why do we need dropouts?
17 minutes
REPO 6. Hands-on Convolutional Networks
• How to implement convnets in PyTorch?
30 minutes
RECIPE 7. Understanding Convolution
40 minutes

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