Course Outline

Introduction to Computer Vision

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

Introduction to Convolutional Neural Networks (CNNs)

  • What are CNNs?
  • Architecture of CNNs: Convolutional layers, pooling, and fully connected layers
  • How CNNs are used in computer vision

Hands-On with TensorFlow and Google Colab

  • Setting up the environment in Google Colab
  • Using TensorFlow for model building
  • Building a simple CNN model in TensorFlow

Advanced CNN Techniques

  • Transfer learning for CNNs
  • Fine-tuning pre-trained models
  • Data augmentation techniques for improved performance

Image Preprocessing and Augmentation

  • Image preprocessing techniques (scaling, normalization, etc.)
  • Augmenting image data for better model training
  • Using TensorFlow’s image data pipeline

Building and Deploying Computer Vision Models

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

Real-World Applications of Computer Vision

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

Summary and Next Steps

Requirements

  • Experience with Python programming
  • Understanding of deep learning concepts
  • Basic knowledge of convolutional neural networks (CNNs)

Audience

  • Data scientists
  • AI practitioners
 21 Hours

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