Welcome to the CNN Image Classification module! This course explores how Convolutional Neural Networks (CNNs) revolutionize image recognition tasks by leveraging spatial hierarchies in data. 📊

📌 Key Concepts

  • Convolutional Layers: Extract local features using filters (kernels)
    Convolutional Layers
  • Pooling Layers: Reduce spatial dimensions while retaining important features
    Pooling Layers
  • Fully Connected Layers: Classify features into final output labels

🛠️ Practical Applications

  1. Object Detection in datasets like CIFAR-10
  2. Medical Imaging analysis for disease diagnosis
  3. Autonomous Vehicles using real-time image classification

🌐 Extend Your Knowledge

For a deeper dive into CNN architectures, check out our CNN Architecture Guide.

CNN Image Classification Examples

Explore related courses: Deep Learning Fundamentals or Advanced NLP Techniques.