This document provides an overview of Convolutional Neural Networks (CNNs). It defines CNNs as networks that learn multi-level features and perform classification in a joint fashion, performing better than traditional approaches for image and video tasks. The document outlines the typical components of a CNN which are convolution, non-linearity, pooling, and fully connected layers. It also discusses properties of CNNs like sparse interactions and parameter sharing. Applications mentioned include image processing, speech recognition, and natural language processing. In conclusion, CNNs represent a major breakthrough in deep learning with many applications, and ongoing research is important to further develop the technology.

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