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Course Outline
Foundations of Computer Vision
- Overview of key computer vision applications
- Understanding image data structures and formats
- Navigating the inherent challenges in computer vision tasks
Introduction to Convolutional Neural Networks (CNNs)
- Defining CNNs and their core functionality
- Examining CNN architecture: convolutional layers, pooling, and fully connected layers
- Understanding the role of CNNs within the computer vision domain
Practical Application: TensorFlow and Google Colab
- Configuring the development environment in Google Colab
- Utilizing TensorFlow for model construction
- Developing a foundational CNN model in TensorFlow
Advanced CNN Methodologies
- Applying transfer learning to CNN architectures
- Fine-tuning pre-trained models for specific tasks
- Employing data augmentation strategies to boost model performance
Image Preprocessing and Data Augmentation
- Techniques for image preprocessing (including scaling, normalization, and more)
- Enhancing model training through image data augmentation
- Leveraging TensorFlow’s image data pipelines
Development and Deployment of Computer Vision Models
- Training CNNs for robust image classification
- Assessing and validating model efficacy
- Integrating models into production environments
Practical Real-World Applications of Computer Vision
- Computer vision implementations in healthcare, retail, and security sectors
- AI-driven object detection and recognition systems
- Utilizing CNNs for facial and gesture recognition
Recap and Future Directions
Requirements
- Proficiency in Python programming
- Solid grasp of deep learning principles
- Foundational understanding of convolutional neural networks (CNNs)
Target Audience
- Data scientists
- AI professionals and practitioners
21 Hours
