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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
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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