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Applied Computer Vision

40 hoursIntermediate7 modules

2 of 7 modules with a published syllabus

0 of 7 modules recorded

Syllabus

Starts from images as data — pixels, channels, OpenCV — and climbs through CNNs (a module shared with Deep Learning), classification with augmentation and object detection with YOLO, ending in segmentation. PyTorch and data cleaning come in as groundwork shared with the trunk.

Modules of this programme

The same objects as the full curriculum. A module opened from any programme that contains it is the same module, with the same syllabus and the same status.

  • CV-101Syllabus published

    Images as data (pixels, channels, OpenCV)

    Detailed syllabus not written yet

  • CORE-104Syllabus published

    Data: collection and cleaning

    Content
    Pandas, CSV/JSON/APIs, missing data, EDA
    Hands-on
    Clean and explore a real, messy dataset

    estimated price0 of 1 module published

  • CORE-203Syllabus published

    PyTorch in practice

    Content
    Tensors, autograd, training loops, GPU
    Hands-on
    Rewrite the CORE-202 network in PyTorch

    estimated price0 of 1 module published

  • DL-201Syllabus published

    CNNs (shared with Computer Vision)

    Detailed syllabus not written yet

  • CV-201Syllabus published

    Classification and augmentation

    Detailed syllabus not written yet

  • CV-202Syllabus published

    Object detection (YOLO)

    Detailed syllabus not written yet

  • CV-301Syllabus published

    Segmentation

    Detailed syllabus not written yet

Not everything fits into 40 hours. State of the art, distributed training, interpretability, edge deployment and the five integrative projects exist only inside the complete tracks: Tracks