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    About CNN

    CNN stands for Convolutional Neural Network, it is a type of artificial neural network specifically designed to process data that has a grid-like topology, such as an image. CNNs are inspired by the visual cortex of animals and the way it processes visual information. They consist of multiple layers of interconnected nodes, called neurons, that are trained to recognize patterns in the input data.

    A CNN typically consists of several layers, including:

    • Input Layer: The input layer receives the raw image data.
    • Convolutional Layer: These layers apply a set of filters to the input image, which scan the image and extract features from it.
    • Pooling Layer: These layers reduce the spatial dimensions of the feature maps generated by the convolutional layers, which helps to reduce the computational cost and make the network more robust to translation invariance.
    • Fully Connected Layer: These layers connect all the neurons in the previous layers and allow the network to learn more complex features from the image.
    • Output Layer: The output layer generates the final prediction of the CNN.

    CNNs are widely used in image classification, object detection, and semantic segmentation tasks. They can also be used in other areas such as natural language processing and speech recognition.

    CNNs are trained using a large dataset of labeled images, and they learn to recognize patterns and features in images during the training process. Once the network is trained, it can be used to make predictions on new images, and it can be fine-tuned to improve its performance on a specific task.

    CNN Based Projects

    • image recognition

    • OCR (Optical character recognition)

    • Object detection for self-driving cars

    • Face recognition on social media

    • Image analysis in healthcare

    • Image tagging

    • Visual Search

    • ​​Recommender engines

    • Predictive Analytics – Health Risk Assessment

    • Predictive Analytics – Drug Discovery

    • image processing

    • classification

    • image segmentation

    • Video analysis

    By |2023-01-20T12:30:21+00:00January 20th, 2023|Categories: Uncategorized|0 Comments

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