Neural Network Training with Convolutional Neural Net (CNN)

btd
10 min readNov 11, 2023
Photo by Vimal S on Unsplash

Convolutional Neural Networks (CNNs) are a class of deep neural networks designed for tasks involving grid-structured data, such as images and video. CNNs have proven highly effective in computer vision tasks, achieving state-of-the-art performance in tasks like image classification, object detection, and image segmentation. Here’s an overview of key concepts related to CNNs:

I. Convolutional Neural Network Architect:

Let’s illustrate the architecture of a Convolutional Neural Network (CNN).

      Input Image

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| Conv Layer |
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| Pooling |
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| Conv Layer |
|________________|

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| Pooling |
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| Conv Layer |
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| Fully |
| Connected |
| Layer |
|________________|

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| Output |
| Layer |
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  • The input is an image, typically represented as a grid of pixel values.
  • Convolutional layers consist of filters (kernels) that slide over the input image to extract features. Each filter detects patterns at different spatial hierarchies.
  • The output of convolutional layers is called feature maps.
  • Pooling layers downsample the spatial dimensions of the feature maps, reducing the amount of information while retaining important features. Common pooling operations include max pooling and average pooling.
  • After several convolutional and pooling layers, the high-level reasoning in the neural network is flattened into a vector and passed through one or more fully connected layers.
  • The fully connected layers combine the learned features and make final predictions.
  • The output layer produces the final predictions based on the learned features. The number of neurons in this layer depends on the task (e.g…

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