Description
Convolutional Neural Networks: The Engine of Modern Computer Vision
This is **Course 4 of Andrew Ng’s Deep Learning Specialization**—the definitive guide to **Convolutional Neural Networks (CNNs)**. You’ll go from understanding filters and pooling layers to building **object detectors, facial recognition systems, and AI-generated art**—all with industry-standard techniques.
What You’ll Build
- A CNN from scratch for image classification (no frameworks)
- A ResNet-50 model using transfer learning for custom datasets
- An object detection system using YOLO-style sliding windows
- A neural style transfer app that merges Van Gogh with your photos
- A face verification system using Siamese networks
Key Topics Covered
- CNN architecture—conv layers, pooling, fully connected layers
- Advanced networks—LeNet, AlexNet, VGG, ResNet, Inception
- Object detection—sliding windows, anchor boxes, IoU
- Face recognition—one-shot learning, triplet loss, embedding spaces
- Neural style transfer—content loss, style loss, optimization
Why Learn CNNs in 2025?
- Computer vision is everywhere—KYC, agritech, healthcare, security
- CNNs are foundational—even transformers often use CNN backbones
- High demand in Nigeria—fintech and healthtech startups need CV engineers
Who Is This For?
- Deep learning students completing the specialization
- Computer vision engineers building real-world systems
- Researchers needing a rigorous CNN refresher
- Developers prepping for CV-focused job interviews
Your Vision AI Journey Starts Here
This course gives you the **math, code, and intuition** to build and debug any CNN-based system.
Enroll now—and see the world through the eyes of AI.
