Applied Deep Learning: Class 14 – Pooling Layers in CNNs

Описание: In this session, we explore pooling layers and how they help convolutional neural networks reduce spatial size while retaining important information. You’ll learn:

📦 What pooling is and why it's used

🖼️ The difference between Max Pooling and Average Pooling

🧠 How pooling reduces dimensions and helps with translation invariance

📐 How pool size and stride affect output shape

🛠️ Hands-on demo: adding pooling layers in Keras / TensorFlow

📊 Visual comparisons of models with vs. without pooling

🔗 Notebook link (for full code & experiments):
https://github.com/GenEd-Tech/Applied...

Perfect for anyone learning CNNs—this class makes pooling clear and practical, not just theory.