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convolutional neural network (CNN, or ConvNet)

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A Presentation on Convolution Neural Networks SESSION 2019-20 Apex Institute of Engineering and Technology, Jaipur Department of Computer Science & Engineering Submitted to: Presented by: Mr. Mohit Saxena rakeshsaran HoD CSE Department 16EAXCS 4th Year (CS) 13 April 2020 Presented by :- Amar Tiwari
Contents: • How a Computer Reads an Image ? • Why not Fully Connected Networks for Image Recognition? • What is Convolution Neural Network? • How Convolutional Network Works? • Convolution. • ReLU. • Pooling. • Fully Connected. • Convolutional Neural Network Use-Case . 23 April 2020 Presented by :- Amar Tiwari
How a Computer Reads an Image ? 33 April 2020 Presented by :- Amar Tiwari
Why Not Fully Connected Networks? 43 April 2020 Presented by :- Amar Tiwari
Why Convolutional Neural Network? • In case of CNN the neuron in a layer will only be connected to small region of the layer before it, instead of all of the neurons in a fully-connected manner. 53 April 2020 Presented by :- Amar Tiwari
What is CNN ? • CNN is a type of feed-forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex. 63 April 2020 Presented by :- Amar Tiwari
How CNN Works? CNN have following layers :  Convolution. ReLU Layer. Pooling. Fully Connected. 73 April 2020 Presented by :- Amar Tiwari
Convolution Layer: 83 April 2020 Presented by :- Amar Tiwari
ReLU Layer: In this layer we remove every negative values from the filtered images and replaces it with zero’s . This is done to avoid the values from summing up to zero . Rectified Linear Unit (ReLU) transforms function only activates a node if the input is above a certain quantity, while the input is below zero, but when the input rises above a certain threshold, it has a linear relationship with the dependent. 93 April 2020 Presented by :- Amar Tiwari
ReLU Transform Function Activation: 103 April 2020 Presented by :- Amar Tiwari
Pooling Layer: In this layer we shrink the image stack into a smaller size. Steps: Pick a window size (usually 2 -3). Pick a stride (usually 2) Walk your window across your Filtered images. From each window, take the maximum value. 113 April 2020 Presented by :- Amar Tiwari
Stacking up the Layers : 123 April 2020 Presented by :- Amar Tiwari
Fully Connected Layer: This is the final layer where the actual classification happens. Here we take our filtered and shrinked images and put them into a single list. 133 April 2020 Presented by :- Amar Tiwari
CNN Use-Case : 143 April 2020 Presented by :- Amar Tiwari
THANK YOU Any Query 153 April 2020 Presented by :- Amar Tiwari

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convolutional neural network (CNN, or ConvNet)

  • 1. A Presentation on Convolution Neural Networks SESSION 2019-20 Apex Institute of Engineering and Technology, Jaipur Department of Computer Science & Engineering Submitted to: Presented by: Mr. Mohit Saxena rakeshsaran HoD CSE Department 16EAXCS 4th Year (CS) 13 April 2020 Presented by :- Amar Tiwari
  • 2. Contents: • How a Computer Reads an Image ? • Why not Fully Connected Networks for Image Recognition? • What is Convolution Neural Network? • How Convolutional Network Works? • Convolution. • ReLU. • Pooling. • Fully Connected. • Convolutional Neural Network Use-Case . 23 April 2020 Presented by :- Amar Tiwari
  • 3. How a Computer Reads an Image ? 33 April 2020 Presented by :- Amar Tiwari
  • 4. Why Not Fully Connected Networks? 43 April 2020 Presented by :- Amar Tiwari
  • 5. Why Convolutional Neural Network? • In case of CNN the neuron in a layer will only be connected to small region of the layer before it, instead of all of the neurons in a fully-connected manner. 53 April 2020 Presented by :- Amar Tiwari
  • 6. What is CNN ? • CNN is a type of feed-forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex. 63 April 2020 Presented by :- Amar Tiwari
  • 7. How CNN Works? CNN have following layers :  Convolution. ReLU Layer. Pooling. Fully Connected. 73 April 2020 Presented by :- Amar Tiwari
  • 8. Convolution Layer: 83 April 2020 Presented by :- Amar Tiwari
  • 9. ReLU Layer: In this layer we remove every negative values from the filtered images and replaces it with zero’s . This is done to avoid the values from summing up to zero . Rectified Linear Unit (ReLU) transforms function only activates a node if the input is above a certain quantity, while the input is below zero, but when the input rises above a certain threshold, it has a linear relationship with the dependent. 93 April 2020 Presented by :- Amar Tiwari
  • 10. ReLU Transform Function Activation: 103 April 2020 Presented by :- Amar Tiwari
  • 11. Pooling Layer: In this layer we shrink the image stack into a smaller size. Steps: Pick a window size (usually 2 -3). Pick a stride (usually 2) Walk your window across your Filtered images. From each window, take the maximum value. 113 April 2020 Presented by :- Amar Tiwari
  • 12. Stacking up the Layers : 123 April 2020 Presented by :- Amar Tiwari
  • 13. Fully Connected Layer: This is the final layer where the actual classification happens. Here we take our filtered and shrinked images and put them into a single list. 133 April 2020 Presented by :- Amar Tiwari
  • 14. CNN Use-Case : 143 April 2020 Presented by :- Amar Tiwari
  • 15. THANK YOU Any Query 153 April 2020 Presented by :- Amar Tiwari