CNN Model Architecture Explained | Convolutional Neural Networks | Deep Learning

Convolutional Neural Networks (CNNs) | CNN Model Architecture
In this video, we dive deep into two fundamental concepts that make CNNs powerful for image processing: local connectivity and parameter sharing.

📌 In this video, we break down the CNN architecture, explaining its key components—convolutions, pooling layers, activation functions, fully connected layers, and more.
🔹 How CNNs work step by step
🔹 Understanding Convolution, Filters, and Feature Maps
🔹 Role of Pooling Layers in CNNs
🔹 Why CNNs outperform traditional neural networks for images

This is the 6th video in our CNN series! Make sure to check out the previous videos to build a solid understanding of CNNs.

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🎯 Who is this for?
Perfect for beginners and intermediates in deep learning who want a structured and practical approach to building AI models. Whether you're prepping for a data science interview or looking to build your own projects, mastering deep learning algorithms will set a strong foundation for more advanced machine learning techniques.

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Time breaks:
0:00 Intro.
0:20 Convolution operation
1:58 CNN architecture
3:01 Feature map shape
4:57 Fully connected layer
6:51 need for pooling layer
7:48 Next steps

If you're learning Machine Learning, Deep Learning, or AI, this video will provide you with a solid foundation to implement your own models. Don't forget to hit like, comment, and subscribe to keep learning with me!
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