Applied Computer Vision Essentials
Learn to build, deploy, and evaluate modern computer vision systems—from classical techniques to cutting-edge deep learning
FormatsVirtual Classroom LiveTopicsAI & machine learning · GK Polaris
What you’ll learn
Foundations & Classical Computer Vision
- Pixels, color spaces, convolution filters
- Lane‑finding with Canny + Hough
- Histogram equalisation & CLAHE
- Low‑light rescue with CLAHE
- Feature extraction: classical descriptors
- Image matching: ORB vs SIFT
- CVAT annotation + COCO export
- Wrap-up: bridging classical to modern CV
Deep Learning for Computer Vision
- Classical to deep transition
- CNN architectures & evolution
- Data‑augmentation strategies
- AutoAugment & RandAugment demo
- Fine‑tune EfficientNet‑V2‑S + Grad‑CAM
- Intro to object detection & YOLO family
- YOLOv11‑nano training start
- Detection metrics & interpretation; TIDE taxonomy
- Model robustness discussion
Advanced Vision: Segmentation & Transformers
- From detection to segmentation
- Segmentation approaches
- SAM 2: promptable segmentation
- SAM 2 segmentation vs YOLO masks
- Vision Transformers revolution
- Video processing fundamentals
- Attention rollout visualisation
- Self-supervised learning
- Fine‑tune DINOv2‑tiny
- Modern CV landscape
- Capstone prep
Modern Applications & Integration
- Recap: CV evolution journey
- Vision-language models
- Image & video generation
- Detector → CLIP → LLM safety report
- Model deployment essentials
- ONNX conversion & optimization
- Production monitoring demo
- Adversarial robustness
- Ethics in Computer Vision
- Wrap-up; Q&A
- Capstone demos
Upcoming training
Dates and availability are confirmed by Bolt when we follow up. All times are shown in the provider’s stated time zone.
2026-10-26 — 2026-10-298:30 AM - 4:30 PM EDT
Virtual Classroom LiveONLINE · English
2026-11-30 — 2026-12-038:30 AM - 4:30 PM EST
Virtual Classroom LiveONLINE · English