CNN Architecture Explained | Custom Model Using Python | Deep Learning | Ahmad Tech

Описание: CNN Model Summary for MNIST Digit Recognition:
We built a Convolutional Neural Network (CNN) to classify handwritten digits from the MNIST dataset. The model consists of three convolutional layers with ReLU activation, followed by max pooling layers to reduce spatial dimensions while retaining important features. The first Conv layer has 32 filters of size 3×3, producing an output of 26×26×32. After the first max pooling, the output reduces to 13×13×32. The second Conv layer has 64 filters, generating 11×11×64, followed by another pooling layer reducing it to 5×5×64. The third Conv layer also has 64 filters, producing 3×3×64. The flatten layer converts the 3D feature maps into a 1D vector of length 576, which is then passed through a dense layer with 64 neurons using ReLU activation. The final dense layer has 10 neurons with softmax activation to output probabilities for each digit (0–9). The total trainable parameters of the model are 93,322. This architecture effectively extracts low-level and high-level features, enabling accurate digit classification.
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In this tutorial, we dive into the core of Deep Learning by building a Convolutional Neural Network (CNN) from scratch. We will walk through the entire process—from loading your dataset to adding convolution and pooling layers, and finally training the model to recognize patterns.

Whether you are a beginner in AI or looking to sharpen your Python skills, this video covers the essential architecture you need to know.

👨‍💻 What you will learn in this video:

How to load and prepare data for Deep Learning.

Understanding Convolutional and Pooling layers.

Building and compiling a custom model in Python.

Training the model and interpreting the summary.

⏱️ Timestamps: 00:00 - Intro 00:25 - Start 06:15 - Data Loading & Preprocessing 10:35 - Model Architecture Setup 10:50 - Convolution & Pooling Layers Explained 15:35 - Training the CNN Model 18:05 - Model Summary & Review 21:31 - Conclusion

📂 Source Code: https://colab.research.google.com/dri...

🎵 Music Credits: Track: Max Brhon - Cyberpunk [NCS Release] Music provided by NoCopyrightSounds. Watch:    • Max Brhon - Cyberpunk | Bass | NCS - Copyr...   Free Download / Stream: http://ncs.io/Cyberpunk

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