This presentation provides an overview of convolutional neural networks (CNNs). It defines CNNs as a class of deep neural networks used primarily for images. The presentation traces the origins and history of CNNs from the 1950s work on visual cortexes to the 2012 AlexNet model. It describes the typical CNN architecture including convolutional layers, pooling layers, and fully connected layers. Examples of CNN applications include image and video recognition. In conclusion, CNNs are a foundational deep learning technique that is increasingly used across domains despite some drawbacks like hardware requirements.

1. Convolutional Neural Network
[A Presentation on]
Presented by
Niloy Sikder

2. Jun 12,
2020
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What is a Convolutional Neural Network (CNN)?
 A class of deep neural networks, mostly used to work with
images.
 A.k.a. ConvNet, CNNs, shift or space invariant artificial
NNs (SIANN).
 Inspired by the visual cortexes of the brain.
 Are regularized versions of multilayer perceptrons.
 Probably the most popular deep learning algorithm.
Fig.1: An abstract of CNN

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Origin and History
 50s, 60s Hubel and Wiesel’s work on cat and monkey
visual cortexes.
 1980 neocognitron, convolutional layers, and
downsampling layers.
 1987 introduction of the first convolutional
network.
 1990 introduction of the concept of max pooling.
 1998 LeNet-5 that could classifies digits.
 1988 SIANN for image character recognition.
 1991 first application in medical image processing and
automatic detection of cancer in mammograms.
 2006 first use of GPUs for CNN implementation.
 2012 AlexNet by Alex Krizhevsky.

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The Architecture of CNN
 CNNs process the input image in multiple layers:
 Convolution layer
 Pooling layer
 Fully connected layer
 The layers follow a feed-forward mechanism.
Fig.2: Layers of CNN
 Normalization (operation)

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What Can CNN Do?
X
O

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Why Do We Need CNNs?

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Operations in a Convolution Layer

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Operations in a Convolution Layer

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Operations in a Convolution Layer

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Operations in a Maxpooling Layer
Maxpooling

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Operations in a Maxpooling Layer
Maxpooling
Maxpooling
Maxpooling
Maxpooling
Maxpooling
Maxpooling

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Normalization
Relu

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Fully Connected Layer

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Arrangement of Layers
Images
Convolution_1
Maxpooling_1
ReLU_1
Convolution_2
Maxpooling_2
ReLU_2
Fully connected
Multilayer
Perceptron
Output

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Example of a CNN Model

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Benefits of Using CNN
 Automatic detection of important features without human
supervision.
 Flexibility in designing the architecture.
 Efficient in terms of memory and complexity.
 Much better performance than the contemporary methods.
Concerns of Using CNN
 Hardware requirement.
 Demands more divert and noise-free data.
 Prone to “overfitting” because of the "fully-
connectedness".
 Hard to find the most optimal network/ architecture.
 Hard to track the internal operations and visualize them.

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Applications
 The go-to model on every image related problem.
 Image and video recognition.
 Natural language processing
 Recommender systems.
 Time-frequency data analysis.
 Decision-based intelligent game development.
Conclusion
 CNN is a foundational deep learning technique..
 Despite the drawbacks, it is being increasingly used by the
researchers of diverse domains to solve decision-making
problems from complex data.

18. Thank You
Any Questions?