Скачать или смотреть CNN | Building Convolutional Neural Networks in Python | Deep Learning Class | Sir Nasir Hussain

Welcome back to the AI & Data Science Course with Sir Nasir Hussain at Saylani Z.A.I.T Park!
This is the next class (CNN Part-2) where we move ahead from basic theory and start understanding how to build CNN models step-by-step using Python & TensorFlow/Keras.

If you watched the previous class (CNN Introduction), this session will take your understanding to the next level.

📘 What We Covered Today (CNN Part-2):

Review of Convolution + Pooling

How CNN learns features (edges → patterns → full objects)

Understanding activation maps & feature maps

Batch size, epochs, learning rate

Building the first CNN model in Keras

Adding layers:

Conv2D

MaxPooling2D

Flatten

Dense

Compiling the model (optimizer, loss, metrics)

Training the CNN model

Visualizing training accuracy & loss

Early overfitting signs and how to handle them

Preparing image datasets for CNN

Real example workflow explained

🎯 Learning Outcomes:

By the end of this class, students will:
✅ Know how to create a CNN model in Python
✅ Understand code structure for deep learning projects
✅ Learn the importance of parameters like filters, kernel size, and pooling
✅ Know how CNNs extract information layer by layer
✅ Be ready for the next class where we train CNN on real image data

📅 Class Number: CNN (Part-2)
👨‍🏫 Instructor: Sir Nasir Hussain
🤖 Course: AI & Data Science
🏫 Institute: Saylani Z.A.I.T Park