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Duration 21 hours
Course Outline
Foundations of Computer Vision
- Survey of computer vision applications
- Analyzing image data structures and formats
- Addressing challenges inherent in computer vision tasks
Basics of Convolutional Neural Networks (CNNs)
- Defining CNNs
- CNN architecture components: Convolutional layers, pooling mechanisms, and fully connected layers
- The role of CNNs in computer vision solutions
Practical Work with TensorFlow and Google Colab
- Configuring the development environment in Google Colab
- Utilizing TensorFlow for model construction
- Assembling a basic CNN model using TensorFlow
Sophisticated CNN Strategies
- Applying transfer learning to CNNs
- Refining pre-trained models through fine-tuning
- Enhancing performance via data augmentation methods
Image Preparation and Augmentation
- Techniques for image preprocessing (including scaling and normalization)
- Expanding image datasets to optimize model training
- Incorporating TensorFlow’s image data pipelines
Construction and Deployment of Computer Vision Models
- Training CNNs specifically for image classification
- Assessing and verifying model performance
- Rolling out models to production settings
Computer Vision in Practical Applications
- Implementation in healthcare, retail, and security sectors
- AI-driven object detection and recognition systems
- Utilizing CNNs for facial and gesture identification
Requirements
- Proficiency in Python programming
- Familiarity with deep learning principles
- Fundamental knowledge of convolutional neural networks (CNNs)
Intended Audience
- Data scientists
- AI practitioners
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3900 € + VAT*
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