An Introduction to convolutional neural network (CNN) in the field of Data Science along with it's implementation.

1. Presented By: Praanav Bhowmik
& Aayush Srivastava
Introduction To CNN

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3. Our Agenda
01 Introduction To Neural
Networks
02 Convolutions
03
Importance Of Pooling
04
Demo
05
05
4
Pooling

4. Introduction to Neural
Network

5. ● Neural networks reflect the behavior of the human brain, allowing computer programs to recognize
patterns and solve common problems in the fields of AI, machine learning, and deep learning.
● Neural networks are comprised of a node layers, containing an input layer, one or more hidden layers,
and an output layer.
● Each node, or artificial neuron, connects to another and has an associated weight and threshold.
● If the output of any individual node is above the specified threshold value, that node is activated, sending
data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.
Introduction to Neural Networks

6. Introduction to Neural Networks

7. Introduction to Neural Networks

8. Introduction to Neural Networks

9. Introduction to CNN

10. ● The convolutional neural network, or CNN for short, is a specialized type of neural network model
designed for working with two-dimensional image data.
● A convolutional neural network, or CNN, is a deep learning neural network sketched for processing
structured arrays of data such as portrayals.
● A convolutional neural network is a feed forward neural network.
CNN

11. Convolutions

12. ● Central to the convolutional neural network is the convolutional layer that gives the network its name.
● The innovation of convolutional neural networks is the ability to automatically learn a large number of
filters in parallel specific to a training dataset under the constraints of a specific predictive modeling
problem.
● We get the most specific feature from the input image, required for classification.
● Ex :- Image Classification
Convolutions

13. Convolutions

14. Pooling

15. ● Pooling layers provide an approach to down sampling feature maps by summarizing the
presence of features in patches of the feature map.
● In simple terms, Pooling is a way of compressing an image.(i.e go over image four pixels at a
time).
● A pooling layer is a new layer added after the convolutional layer.
● The pooling layer operates upon each feature map separately to create a new set of the
same number of pooled feature maps.
● Pooling involves selecting a pooling operation, much like a filter to be applied to feature
maps. The size of the pooling operation or filter is smaller than the size of the feature map.
Pooling

16. Pooling

17. ● There is a limitation of the feature map output of convolutional layers is that they record
the precise position of features in the input.
● This means that small movements in the position of the feature in the input image will result
in a different feature map.
● This can happen with re-cropping, rotation, shifting, and other minor changes to the input
image.
Why use Pooling with Convolution

18. Why use Pooling with Convolution

19. Why use Pooling with Convolution

20. Applications of CNN

21. ● Decoding Facial Recognition
● Analysing Documents
● Understanding Climate
● Advertising
● Understanding Gray Areas
Applications of CNN

22. Code for Pooling and Convolutional Layer

23. Demo

24. ● Convolutional Neural Networks in TensorFlow by DeepLearning.AI
● Machine Learning Mastery
● Towards Data Science
References

25. Thank You !
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