🔥 Convolutional Neural Networks: Clearly explained!

🖼 Convolutional Neural Networks (CNNs): CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition.

➡️ Feedforward Process: Data flows from input to output layers. Images undergo convolution operations, ReLu activation, and Max-Pooling to reduce size and enhance translation and scaling invariance. Finally, data is classified through a fully connected network.

🔄 Training Process: The training involves batches, backpropagation, and gradient descent to minimize errors. The weights start with random values and are updated through backpropagation. This cycle repeats until accuracy is achieved.

📊 Use Cases: CNNs excel in processing images, videos, and audio for tasks like classification, segmentation, and object detection.

⚠️ Limitations: While CNNs handle translation and scaling well, they struggle with rotation invariance.

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