Build a Convolutional Neural Network (CNN) 🧠🖼️

Welcome to the CNN Construction Guide! This tutorial will walk you through building a Convolutional Neural Network from scratch using Python and TensorFlow/Keras.

🧩 Key Components of a CNN

1. Convolutional Layers

   • Extract features using filters (kernels)
   • Example: Conv2D(filters=32, kernel_size=(3,3), activation='relu')

   <center><img src="https://cloud-image.ullrai.com/q/Convolutional_Layer/" alt="Convolutional Layer"/></center>  
   

2. Pooling Layers

   • Reduce spatial dimensions (e.g., MaxPooling2D)
   • Helps with translation invariance

   <center><img src="https://cloud-image.ullrai.com/q/Pooling_Layer/" alt="Pooling Layer"/></center>  
   

3. Fully Connected Layers

   • Flatten features and add classification logic
   • Use Dense layers for final output

🛠️ Step-by-Step Build Process

1. Import Libraries

   import tensorflow as tf  
   from tensorflow.keras import layers, models  
   

2. Create Model Architecture

   model = models.Sequential([  
       layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),  
       layers.MaxPooling2D((2,2)),  
       layers.Conv2D(64, (3,3), activation='relu'),  
       layers.MaxPooling2D((2,2)),  
       layers.Flatten(),  
       layers.Dense(64, activation='relu'),  
       layers.Dense(10, activation='softmax')  
   ])  
   

3. Compile and Train

   model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])  
   model.fit(train_images, train_labels, epochs=5)  
   

📚 Extend Your Learning

• Explore CNN Applications in Computer Vision
• Dive deeper into activation functions

<center><img src="https://cloud-image.ullrai.com/q/CNN_Architecture/" alt="CNN Architecture"/></center>