Overview
Learn classical and deep learning approaches for images and video: convolutions, detection, segmentation, and efficient training workflows. The bootcamp emphasizes reproducible experiments and clear evaluation metrics.
Who it's for
- ML practitioners specializing in vision
- Robotics and perception engineers leveling up
- Builders preparing for CV-heavy interviews
What you will learn
Curriculum breakdown, from foundations to portfolio-ready work.
CV foundations
Pixels to features to networks.
- Convolutions, pooling, and receptive fields
- Data augmentation and training stability
- Metrics: mAP, IoU, and calibration basics
Detection & segmentation
Architectures used in industry.
- Object detection families and trade-offs
- Instance vs semantic segmentation
- Lightweight models for edge constraints
Projects & optimization
Ship something measurable.
- Training loops, mixed precision, and debugging
- Exporting models for inference
- Capstone: CV pipeline with report and ablations
Key features
- Primarily live labs with recordings
- GPU-backed project environment guidance
- Weekly mentor critiques on experiments
- Peer review on write-ups and figures
- Interview drills on architecture choices
Projects
Detector fine-tune
Fine-tune a detector on a curated dataset with clear metrics and error analysis.
Segmentation capstone
Segmentation model with qualitative and quantitative evaluation.
Outcomes
Skills gained
Train, debug, and evaluate vision models with professional rigor.
Job readiness
Explain design decisions and failure modes in panel interviews.
Portfolio
Vision projects with strong visuals and experiment logs.
Your mentor
Elena Vasquez
Principal CV Engineer · Autonomy
14+ years in vision and robotics
Elena has led perception teams and cares deeply about clear metrics, clean datasets, and honest ablations.
Duration & schedule
10 weeks
14–18 hours
Live sessions 3× weekly; project studios on weekends.
Ready to join the next cohort?
Secure your seat or request the full syllabus. We'll confirm prerequisites and start dates.
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