The document discusses Convolutional Neural Networks (CNNs). It explains that CNNs are a type of neural network that use convolutional operations in at least one layer. CNNs are well-suited for image classification and segmentation problems. The key layers in a CNN are convolutional layers, pooling layers, flattening layers, and fully connected layers. Convolutional layers act as feature extractors, pooling layers reduce spatial size, flattening layers transform pooled features into a vector, and fully connected layers are for classification.

1. Deep Learning
Classification
CNN
M. Abrar Ahmad

2. DEEP LEARNING
Multiple definitions, however, these definitions have in common:
 Multiple layers of processing units
 Supervised or unsupervised learning of feature representations in
each layer, with the layers forming a hierarchy from low-level to
high-level features
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3. DEEP LEARNING
 Deep Learning has proved to
be a very powerful tool because
of its ability to handle large
amounts of data
 The interest to use hidden
layers has surpassed
traditional techniques,
especially in pattern
recognition
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4. DEEP LEARNING
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Deep
Learning
Artificial Neural
Network
(ANN)
Convolutional Neural
Network
(CNN)
Recurrent Neural
Network
(RNN)

5. 5
What are the CNNs???
 Convolutional Neural Network (CNN) is a multi-layer neural
network
 Neural Networks with a Convolutional operation in at least one of
the layers
 CNN performs much better than traditional approaches for various
image classification and segmentation problems
 CNN is used to reduce the images into a form that is easier to
process, without losing features that are critical for getting a good
prediction

6. 6
Applications of CNN
 Face Recognition
 Image Classification
 Object Detection
 Segmentation
 Self-driving cars that leverage CNN based vision systems.
 And many More

7. Drawbacks of CNN
 A Convolutional neural network is significantly slower due to
different operations such as maxpooling.
 If the CNN has several layers, then the training process takes a lot of
time if the computer doesn’t consist of a good GPU
 A ConvNet requires a large Dataset to process and train the neural
network
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8. Layers in CNN
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9. Convolutional Layer
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 Convolutional layer acts as a feature extractor that extracts features of the
inputs such as edges, corners , endpoints
 This image shows what a convolution is
 We take a filter/kernel(3×3 matrix) and apply it to the input image to get
the convolved feature

10. Convolutional
 The convolved feature is passed on to the next layer
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11. The Convolutional operation
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Output Feature Map Size
 Feature Map Size = 1+ (W – F + 2P)/S
 Input Size (W): 5 x 5
 Filter Size (F): 3 X 3
 Stride (S): 1
 Padding (P): 0
 So,
 1 + (5 – 3 + 0)/1 = 3

13. Normalization
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 Once we get the output, we shall apply Rectified Linear Unit (relu)
activation function on each field of output
 After applying the ReLU a stack of images become a stack of
images with no negative values
 Max(y,0)

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Pooling Layer
 The Pooling layer is responsible for reducing the spatial size of
the Convolved Feature
 This is to decrease the computational power required to process
the data by reducing the dimensions
 There are two types of pooling
 max pooling
 average pooling

15. Max Pooling
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 The most common approach used in pooling is max pooling
 in Max Pooling is we find the maximum value of a pixel from a
portion of the image covered by the kernel
 Max Pooling also performs as a Noise Suppressant
 Pooling Filter Size = 3 X 3
 Stride = 1

16. Avg Pooling
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 Average Pooling returns the average of all the values from the
portion of the image covered by the Kernel
 Average Pooling simply performs dimensionality reduction as a
noise suppressing mechanism
 We can say that Max Pooling performs a lot better than Average
Pooling
 Pooling Filter Size = 3 X 3
 Stride = 1

17. Pooling
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 Pooling Filter Size = 2 x 2
 Stride = 2

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Flattening Layer
 Once the pooled featured map is obtained, the next step is to
flatten it
 We flatten the output of the convolutional layers to create a
single long feature vector
 Flattening involves transforming the entire pooled feature map
matrix into a single column which is then fed to the neural
network for processing

19. Flattening
 we are literally going to
flatten our pooled feature
map into a column like in the
image below
 it is connected to the final
classification model, which is
called a fully-connected layer
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20. Deep stacking
 Layers can be repeated many time
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Fully Connected Layer
 A traditional Multi-Layer Perceptron
 The term “Fully Connected” implies that every neuron in the previous layer
is connected to every neuron on the next layer
 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
 Fully connect layer act as classifier

22. Fully Connected
 A summation of product of inputs and weights at each output node
determines the final prediction
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23. CNN
 Putting all layers of CNN together
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