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 Duration 21 hours

Course Outline

Foundations of Computer Vision

  • An overview of computer vision applications
  • Understanding image data structures and formats
  • Key challenges faced in computer vision tasks

Introduction to Convolutional Neural Networks (CNNs)

  • Defining CNNs
  • CNN architecture: convolutional layers, pooling, and fully connected layers
  • The role of CNNs in computer vision

Practical TensorFlow and Google Colab

  • Configuring the environment in Google Colab
  • Employing TensorFlow for model construction
  • Creating a basic CNN model with TensorFlow

Advanced CNN Methodologies

  • Applying transfer learning to CNNs
  • Fine-tuning pre-trained models
  • Data augmentation strategies for enhanced performance

Image Preprocessing and Augmentation

  • Image preprocessing methods (such as scaling and normalization)
  • Enhancing image data for superior model training
  • Leveraging TensorFlow’s image data pipeline

Developing and Deploying Computer Vision Models

  • Training CNNs for image classification
  • Assessing and validating model performance
  • Implementing models in production environments

Practical Computer Vision Applications

  • Computer vision in healthcare, retail, and security sectors
  • AI-driven object detection and recognition
  • Utilizing CNNs for facial and gesture recognition

Requirements

  • Proficiency in Python programming
  • Comprehension of core deep learning concepts
  • Familiarity with the fundamentals of convolutional neural networks (CNNs)

Target Audience

  • Data scientists
  • AI practitioners

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