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WA3519

AI Computer Vision Training

This AI Computer Vision course gives attendees a deep understanding of computer vision concepts, techniques, and applications. The course covers everything from image processing fundamentals and feature extraction to advanced deep learning architectures and object detection. Participants gain hands-on experience implementing computer vision algorithms and building end-to-end applications using popular frameworks and libraries.

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Course Details

Duration

3 days

Prerequisites

  • Strong programming skills in Python and familiarity with machine learning concepts
    • Pandas, NumPy, scikit-learn
  • Basic understanding of linear algebra recommended (vectors)
  • Foundations of machine learning - classification, regression, clustering, etc
    • Feature engineering, feature selection, data pre-processing for ML
    • Model metrics and evaluation - MSE, R-squared, Precision, Recall, etc

Skills Gained

  • Understand the fundamental concepts and techniques in computer vision, including image processing, feature extraction, and image classification with Deep Learning
  • Implement advanced deep learning architectures for computer vision tasks, such as CNNs, R-CNNs
  • Apply transfer learning and fine-tuning techniques to build accurate and efficient computer vision models
  • Design and implement end-to-end computer vision applications for real-world use cases, such as autonomous vehicles and facial recognition systems
Course Outline
  • Introduction to Computer Vision
    • Overview of computer vision and its applications
    • Digital image fundamentals and representation
    • Image processing techniques (e.g., filtering, enhancement, and restoration)
  • Feature Extraction and Image Classification
    • Traditional feature extraction techniques (e.g., SIFT, SURF, and HOG)
    • Bag-of-words model and feature aggregation techniques
    • Classic image classification algorithms (e.g., SVM and k-NN)
  • Deep Learning for Computer Vision
    • Introduction to convolutional neural networks (CNNs)
    • Popular CNN architectures (e.g., LeNet, AlexNet, VGGNet, and ResNet)
    • Transfer learning and fine-tuning techniques for computer vision tasks
  • Object Detection and Segmentation
    • Object detection architectures (e.g., R-CNN, Fast R-CNN, and Faster R-CNN)
    • Single-shot object detectors (e.g., YOLO and SSD)
    • Semantic and instance segmentation techniques (e.g., FCN and Mask R-CNN)
  • Advanced Computer Vision Techniques
    • Attention mechanisms and transformer architectures for computer vision
    • Generative models for image synthesis and augmentation (e.g., GANs and VAEs)
    • Unsupervised and self-supervised learning techniques for computer vision
  • Computer Vision Applications and Case Studies
    • Autonomous vehicles and robotics
    • Facial recognition and biometric systems
    • Medical imaging and diagnosis
    • Retail and e-commerce applications (e.g., product recognition and visual search)