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Course Outline
Advanced CNN Techniques
Building and Deploying Computer Vision Models
Hands-On with TensorFlow and Google Colab
Image Preprocessing and Augmentation
Introduction to Computer Vision
Introduction to Convolutional Neural Networks (CNNs)
Real-World Applications of Computer Vision
Summary and Next Steps
- Computer vision in healthcare, retail, and security
- AI-powered object detection and recognition
- Using CNNs for face and gesture recognition
- Image preprocessing techniques (scaling, normalization, etc.)
- Augmenting image data for better model training
- Using TensorFlow’s image data pipeline
- Overview of computer vision applications
- Understanding image data and formats
- Challenges in computer vision tasks
- Setting up the environment in Google Colab
- Using TensorFlow for model building
- Building a simple CNN model in TensorFlow
- Training CNNs for image classification
- Evaluating and validating model performance
- Deploying models to production environments
- Transfer learning for CNNs
- Fine-tuning pre-trained models
- Data augmentation techniques for improved performance
- What are CNNs?
- Architecture of CNNs: Convolutional layers, pooling, and fully connected layers
- How CNNs are used in computer vision
Requirements
Audience
- Data scientists
- AI practitioners
- Experience with Python programming
- Understanding of deep learning concepts
- Basic knowledge of convolutional neural networks (CNNs)
21 Hours
Testimonials (1)
I genuinely enjoyed the hands-on approach.
