Convolutional Neural Networks | Computer Vision

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Last updated on June 22, 2025 4:29 am

Learn the process of obtaining revolutionary results in computer vision such as object detection and image segmentation using convolutional neural networks.

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Convolutional neural networks form the crux of most sophisticated computer vision applications such as auto-tagging on Facebook and facial security features. Are you curious to learn about the software behind popular technologies such as Siri and Google Translate? This free online course will satisfy your curiosity by explaining the features of neural networks in processing sequences such as text, sound, videos and images.

What You Will Learn In This Free Course

  • Outline the role of convolutional ne…
  • Discuss the application of backpropa…
  • Identify the various CNN architectur…
  • Describe the methods for interpretin…
  • Outline the role of convolutional neural networks (CNNs) in analyzing images
  • Discuss the application of backpropagation in CNNs
  • Identify the various CNN architectures used for image classification
  • Describe the methods for interpreting and understanding the applications of CNNs
  • Explain the features of CNNs in image classification
  • Identify the prospects of the various features for discriminative localization
  • Describe the methods of identifying and locating objects within an image
  • Explain the process of splitting an image into different regions
  • Summarize the process of segmenting images using CNNs
  • Explain the role of recurrent neural networks (RNNs) in modeling sequence data
  • Define what exploding and vanishing gradient problems are in RNNs
  • Describe the methods of processing videos using CNNs and RNNs
  • Introduction to Convolutional Neural Networks

    This module illustrates the connection between convolution and neural networks by considering the task of image classification. You will discover how the computer can perform image classification by looking for low-level features such as edges and curves and then building up to more abstract concepts through a series of convolutional layers.

    Visualization and Understanding CNN’s

    This module illustrates several approaches for understanding and visualizing convolutional neural networks in predicting models. You will discover how CNN using deep learning techniques outperformed the other methods in analysing images and interpreting and understanding the applications of convolutional neural networks in analysing images.

    First Course Assessment; Module

    Object Detection and Segmentation using CNN’s

    This module explains the process of identifying parts of the image and understanding what object they belong to. You will discover the process of estimating the body?s configuration from a single monocular image and the method of identifying unexpected items or events in images that differ from the pattern.

    Recurrent Neural Networks

    This module illustrates the underlying principle of recurrent neural networks saving the output of a particular layer and feeding this back to the input to predict the output of the layer. You will explore how recurrent neural networks can perform the same task from the output of the previous data of a series of sequence data.

    Second Course Assessment; Module

    Course assessment

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      Convolutional Neural Networks | Computer Vision
      Convolutional Neural Networks | Computer Vision
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