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Convolutional Neural Networks (CNNs)

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[Convolutional Neural Networks - Medium]

- Overview

A convolutional neural network (CNN) is a network architecture that uses deep learning (DL) to learn from data directly. CNNs are used for image recognition and tasks that involve processing pixel data. They are also effective for classifying audio, time-series, and signal data. 

CNNs are feed-forward neural networks that process data with a grid-like topology. The layers are arranged so that they detect simpler patterns first, such as lines and curves, and more complex patterns further along. As the image data progresses through the layers, it starts to recognize larger elements or shapes of the object. 

CNNs are powerful tools but require millions of labeled data points for training. 


- Three Main Types of Layers

Convolutional neural networks (CNNs) are a type of neural network that use three-dimensional data to perform image classification and object recognition tasks. CNNs are a subset of machine learning and are at the core of deep learning algorithms.

CNNs have three main types of layers, with the convolutional layer being the first layer and the core building block of a CNN. The layers are arranged so that they detect simpler patterns first (lines, curves, etc.) and more complex patterns (faces, objects, etc.) further along. 

CNNs are made up of three main types of layers:

  • Convolutional layer: The first layer of a CNN, and the core building block of a CNN. This is where the majority of computation occurs.
  • Pooling layer: CNNs use a series of convolution and pooling layers to extract features from images and videos.
  • Fully connected (FC) layer: There can be multiple convolutional and pooling layers. The more layers in the network, the greater the complexity and (theoretically) the accuracy of the machine learning model.

CNNs have several advantages, including: 

  • Good at detecting patterns and features in images, videos, and audio signals.
  • Robust to translation, rotation, and scaling invariance.
  • End-to-end training, no need for manual feature extraction.


CNNs are distinguished from other neural networks by their superior performance with image, speech, or audio signal inputs. They are particularly useful for finding patterns in images to recognize objects, classes, and categories. They can also be quite effective for classifying audio, time-series, and signal data. 


- Applications of CNNs

CNNs have a wide range of real-world applications, including:

  • Healthcare: Assisting in medical diagnostics and imaging
  • Automotive: Self-driving cars, automated cruise control, and parking assistance
  • Social media: Image analysis tasks, such as suggesting people to tag in photographs or flagging potentially offensive images for moderation
  • Retail: Visual search systems and recommender systems
  • Virtual assistants: Recognizing spoken keywords and helping interpret users' commands

CNNs fall under the supervised learning category of neural networks. This means that the network requires a set of data that is already classified into the required classes.

[More to come ...]
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