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 Duration 21 hours

Course Outline

Foundations of Computer Vision

  • An overview of diverse computer vision applications
  • Understanding image data structures and formats
  • Addressing common challenges in computer vision tasks

Convolutional Neural Networks (CNNs) Explained

  • Defining the role of CNNs in deep learning
  • Examining CNN architecture: convolutional layers, pooling mechanisms, and fully connected layers
  • Applications of CNNs in computer vision workflows

Practical Implementation with TensorFlow and Google Colab

  • Configuring the development environment in Google Colab
  • Applying TensorFlow for model construction
  • Creating a basic CNN model using TensorFlow

Advanced Methodologies for CNNs

  • Implementing transfer learning for CNN architectures
  • Techniques for fine-tuning pre-trained models
  • Applying data augmentation to enhance model performance

Image Preprocessing and Augmentation Strategies

  • Essential preprocessing methods such as scaling and normalization
  • Augmenting image datasets to improve model robustness
  • Leveraging TensorFlow’s image data pipeline

Development and Deployment of Vision Models

  • Training CNNs specifically for image classification
  • Methods for evaluating and validating model accuracy
  • Strategies for deploying models into production environments

Real-World Applications of Computer Vision

  • Use cases in healthcare, retail, and security sectors
  • AI-driven object detection and recognition systems
  • Applying CNNs for face identification and gesture recognition

Requirements

  • Proficiency in Python programming
  • Familiarity with core deep learning principles
  • Fundamental understanding of convolutional neural networks (CNNs)

Target Audience

  • Data scientists
  • AI practitioners

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