Covers: implementation of Convolutional Neural Network

- How to implement convnets in PyTorch?

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

Amir Hajian

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Contributors

- 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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