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 Duration 21 hours

Course Outline

Overview of Computer Vision Systems

  • Scope of computer vision applications in the public sector
  • Analysis of image data structures and standard formats
  • Key challenges in operationalizing computer vision tasks

Foundations of Convolutional Neural Networks (CNNs)

  • Definition and function of CNNs
  • CNN architecture components: convolutional layers, pooling mechanisms, and fully connected layers
  • Application of CNNs within computer vision workflows for government

Practical Implementation with TensorFlow and Google Colab

  • Configuration of the development environment in Google Colab
  • Utilizing TensorFlow for the construction of models
  • Development of a basic CNN architecture using TensorFlow

Advanced Methodologies in CNN Development

  • Implementation of transfer learning in CNN frameworks
  • Optimization of pre-trained models through fine-tuning
  • Application of data augmentation strategies to enhance model performance

Image Preprocessing and Data Augmentation

  • Standard preprocessing methods, including scaling and normalization
  • Techniques for augmenting image datasets to improve training efficacy
  • Integration of TensorFlow’s image data pipelines

Development and Deployment of Computer Vision Models

  • Training CNNs for accurate image classification
  • Assessment and validation of model accuracy and reliability
  • Deployment of models into production environments for government use

Operational Applications of Computer Vision

  • Application of computer vision in healthcare, retail, and public security
  • AI-driven object detection and identification systems
  • Utilization of CNNs for facial recognition and gesture interpretation

Course Summary and Future Directions

Requirements

  • Proficiency in Python programming
  • Foundational understanding of deep learning principles
  • Basic familiarity with convolutional neural network (CNN) architecture

Intended Audience

  • Data scientists
  • AI practitioners

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