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 Duration 21 hours

Course Outline

Foundations of Computer Vision

  • Survey of current computer vision applications
  • Insight into image data structures and formats
  • Key challenges encountered in computer vision tasks

Essentials of Convolutional Neural Networks (CNNs)

  • Defining the role and nature of CNNs
  • CNN architecture breakdown: convolutional layers, pooling mechanisms, and fully connected layers
  • The application of CNNs in solving computer vision problems

Practical Implementation with TensorFlow and Google Colab

  • Configuring the development environment within Google Colab
  • Leveraging TensorFlow for streamlined model construction
  • Constructing a basic CNN model using TensorFlow

Advanced Strategies for CNNs

  • Applying transfer learning to accelerate CNN development
  • Optimizing pre-trained models through fine-tuning
  • Implementing data augmentation techniques to improve model robustness

Image Preprocessing and Data Augmentation

  • Core preprocessing methods such as scaling and normalization
  • Enhancing dataset diversity through image augmentation for better training
  • Utilizing TensorFlow’s specialized image data pipelines

From Development to Deployment: Computer Vision Models

  • Training CNNs effectively for image classification tasks
  • Conducting rigorous evaluation and validation of model performance
  • Preparing and deploying models for production environments

Applications of Computer Vision in the Real World

  • The impact of computer vision in healthcare, retail, and security sectors
  • Implementing AI-driven object detection and recognition systems
  • Utilizing CNNs for advanced face and gesture recognition technologies

Requirements

  • Proficiency in Python programming
  • A solid grasp of deep learning principles
  • Familiarity with the fundamentals of convolutional neural networks (CNNs)

Intended Audience

  • Data scientists
  • AI practitioners

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