Description
Advanced Computer Vision: From Research Papers to Production Systems
You know CNNs—but can you deploy ResNet on mobile, detect objects in real-time with SSD, or transfer artistic styles to video? This course bridges the gap between academic breakthroughs and real-world computer vision engineering.
What You’ll Build
- London Underground sign detector using SSD and TensorFlow
- Facial emotion, age, and gender recognizer for surveillance or UX
- Neural Style Transfer app that paints your photos in Van Gogh’s style
- Medical nuclei segmenter using U-Net for cancer detection
- Simpsons character classifier with custom CNN (LittleVGG)
Architectures You’ll Master
- VGG & Transfer Learning—leverage pre-trained models for custom tasks
- ResNet & Inception—solve vanishing gradients in deep networks
- SSD (Single Shot Detector)—real-time object detection
- U-Net—precise image segmentation for medical imaging
- Neural Style Transfer—combine content and style from different images
Why This Course Stands Out
- No toy datasets—work with real-world images: street signs, faces, medical slides.
- Deployment focus—learn to optimize models for speed and memory.
- Debugging included—visualize filters, heatmaps, and salience maps to understand what CNNs “see.”
Who Should Take This?
- Intermediate CV developers stuck on basic classification
- Deep learning engineers preparing for CV interviews
- Researchers implementing SOTA architectures
- Freelancers building custom vision solutions
Your Computer Vision Career Accelerator
This course doesn’t just teach models—it teaches how to choose, adapt, and deploy them. You’ll graduate with a portfolio that proves you can solve hard, real-world vision problems.
Ready to move from “Hello World” to “Production Ready”? Enroll now.
