Convolutional neural networks (CNNs) are a type of neural network designed to process images. CNNs use a series of convolution and pooling layers to extract features from images. Convolution multiplies the image with filters to produce feature maps, while pooling reduces the size of the representation to reduce computation. This process allows the network to learn increasingly complex features from the input image and classify it. CNNs have applications in areas like facial recognition, document analysis, and image classification.

1. Intro To Convolutional Neural
Networks
Presenters :
Basit Rafiq
Sunil Ashraf

2.  Convolutional Neural Network
 (CNN) is a type of artificial neural network or
algorithm used in image recognition and processing
that is specifically designed to process IMAGES.

3. Why CNNs?

4. CNN Model

5. Convolution
This Process is the
multiplication of the
given image matrix
with the required
filter.
It makes the input of
our next step pooling.

6. Convolution Process

8. Pooling process
Pooling. Its function is to progressively
reduce the image size of the
representation to reduce the amount of
computation in the network.
Types
1) Max Pooling
2) Average Pooling
3) Sum Pooling

9. Pooling Calculation
Max Pooling

10. Results

11. Traditional Approach To
Image
Classification
Input Image preprocessing Classifier Object Label

12. https://papers.nips.cc/paper/4824-‐imagenet-‐classification-‐with-‐deep-‐convolutional-‐neural-‐networks
Image Classification

13. Generate An Image From A Sketch
https://affinelayer.com/pixsrv/

14. Part of Preprocessing (rotation)

15. Object Recognition
• This animal is a combination of
arms, legs, & a tail in specific
proportions.
• Similarly recognizing trees,
grass..etc (place)
• Object recognition process

16. Image Features
• Image recognition is something recognizing
the different angles of the image.
• Edges
• Lines at different angles, curves, etc.
• Colors, or patterns of colors etc..

17. CNN Layer Architecture
Pooling (optional)
Labeling
Convolution
Input (Image)

18. Ideally We’d Learn Features
Input
Image
CNNs
Output
Label

19. Applications of CNN
1.Decoding Facial Recognition
2.Analyzing Documents
3. Environmental Collections
4.Advertising
5.Recognizing Images
6.Many more

20. Questions
?