Convolutional Neural network and its applications

1. ” Convolutional Neural Networks (CNN)”
University of Manouba.
Tunis Higher School of Business
Preparedby:
Sirine BEN AMMAR
2023-2024
Sirine BEN AMMAR

2. Outline
1 General context
5 CNN Components
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CNN Architecture
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Applications of CNN models
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Implementation
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3 Properties of CNN models
Definition
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Some recent articles
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Conclusions
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3. Context
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4. Context
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Machine Learning VS Deep Learning

5. Context
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6. Context
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7. Definition
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❑Convolutional Neural
Networks (CNNs) learns
multi-level features and
classifier in a joint fashion and
performs much better than
traditional approaches for
various image classification
and segmentation problems.

8. Properties of CNN models
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➢Sparseinteractions between NN units (through kernels of small
size)
✓fewer parameters to learn
✓less computation resources arerequired
➢Parameter sharing (samekernel is applied throughout theinput)
✓Maintain the same feature detection throughout the
input.
➢Ability to (automatically) learn local structure
➢Can handle variable-sized inputs.

9. CNN Architecture
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➢Typically, a CNN model consists of convolution layers, for feature selection,
followedby fullyconnected layers that perform the prediction task.

10. CNN Architecture
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11. CNN Components
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Convolution
Non Linearity
Pooling or Sub Sampling
Classification (Fully Connected Layer)
➢There are 4 components in the CNN:

12. CNN Components
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Input:
•An image is a matrix of pixel values.
➢If we consider a gray scale image, the
value of each pixel in the matrix will range
from 0 to 255.
➢If we consider an RGB image,
each pixel will have the combined
values of R, G and B.

13. CNN Components
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Convolution Non Linearity Pooling Classification
1. Convolution :
➢The primary purpose of convolution in case of a CNN is to extract
features from the input image.

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Convolution Non Linearity Pooling Classification
2. Non Linearity (ReLU):
➢ Replaces all negative pixel values in the
feature map by zero.
➢ The purpose of ReLU isto introduce
non-linearityin CNN, since most of the
real-world data would be non-linear.
➢ Other non-linearfunctions such as
tanh (-1, 1) or sigmoid (0, 1) can also
be used instead of ReLU (0, input).

15. CNN Components
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Convolution Non Linearity Pooling Classification
3. Pooling:
➢ Reduces the dimensionalityof each feature map but retains the most
important information.

16. CNN Components
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Convolution Non Linearity Pooling Classification
4. Fully Connected Layer:
➢ The term “Fully Connected” impliesthat every neuron inthe previous
layer is connected to every neuron on the next layer.
➢ Their activations can hence be computed
with a matrix multiplication followed by a
bias offset.
➢ The purpose of the fully connected layer
is to use the high-level features for classifying
the input image into various classes based on
the training dataset.

17. Applications of CNN models
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➢Image Processing
✓image classification
✓object detection
✓image segmentation
✓object tracking
✓face recognition…
➢Speech Processing
➢Text Detections and Recognition (OCR)
➢Natural Language Processing
➢Drug Discovery
➢Timeseries Analysis
✓Health risk assessment
✓Electromyography (EMG)
recognition…

18. Implementation
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Link: https://colab.research.google.com/drive/17Svx0pQE_0g-
4uz22W_F0nFdsySiurt0

19. Some recent articles
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20. Some recent articles
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21. Conclusions
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➢In conclusion, Convolutional Neural Networks represent a major
breakthrough in deep learningwith vast and varied applications.
➢As we wrap up this presentation, let's look to the future: new
challenges, technological advancements, and extendedapplications.
➢Ongoing commitment to research and development is crucial to
fully harness the potential of thisever-evolving technology.
Sirine BEN AMMAR

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Thank you!