🎯 What is a Convolutional Neural Network?
A CNN is a type of deep learning model designed for processing grid-like data (e.g., images). It excels in tasks like image recognition, object detection, and pattern analysis through convolutional layers, pooling layers, and fully connected layers.

Key Components of CNN

  • Convolutional Layer
    Applies filters to detect features (edges, textures) in images.

    Convolutional_Layer

  • Pooling Layer
    Reduces spatial dimensions (e.g., Max Pooling) to capture invariance.

    Pooling_Layer

  • Fully Connected Layer
    Classifies features into final output (e.g., labels).

    Fully_Connected_Layer

Applications of CNN

  • 🧠 Image Recognition
    Used in facial detection, medical imaging, and security systems.

    Image_Recognition

  • 📸 Object Detection
    Identifies objects within images (e.g.,YOLO, SSD models).

    Object_Detection

  • 📊 Data Analysis
    Analyzes 2D/3D data (e.g., satellite imagery, time-series).

    Data_Analysis

Code Example with TensorFlow

import tensorflow as tf  
model = tf.keras.Sequential([  
  tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),  
  tf.keras.layers.MaxPooling2D(2,2),  
  tf.keras.layers.Flatten(),  
  tf.keras.layers.Dense(10, activation='softmax')  
])

🔗 Learn more about TensorFlow CNNs

Extend Your Knowledge

💡 Pro Tip: Use PyTorch for flexible CNN experimentation!

PyTorch