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 Duration 21 hours

Course Outline

Foundations of Computer Vision

  • Overview of computer vision applications
  • Understanding image data and formats
  • Challenges associated with computer vision tasks

Introduction to Convolutional Neural Networks (CNNs)

  • Definition and role of CNNs
  • CNN architecture: Convolutional layers, pooling, and fully connected layers
  • The application of CNNs in computer vision

Practical Work with TensorFlow and Google Colab

  • Configuring the environment in Google Colab
  • Using TensorFlow for model construction
  • Building a basic CNN model with TensorFlow

Advanced CNN Methods

  • Transfer learning strategies for CNNs
  • Fine-tuning pre-trained models
  • Data augmentation techniques to improve performance

Image Preprocessing and Augmentation

  • Image preprocessing methods (scaling, normalization, etc.)
  • Enhancing image data for better model training
  • Leveraging TensorFlow’s image data pipeline

Development and Deployment of Computer Vision Models

  • Training CNNs for image classification
  • Evaluating and validating model performance
  • Deploying models into production environments

Practical Applications of Computer Vision

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

Requirements

  • Proficiency in Python programming
  • Foundational understanding of deep learning concepts
  • Basic familiarity with convolutional neural networks (CNNs)

Audience

  • Data scientists
  • AI practitioners

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