Convolutional Neural Networks (CNN) — How AI Sees Images — Free AI & ML Tutorial
Learn Convolutional Neural Networks (CNN) — How AI Sees Images in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Convolutional Neural Networks (CNN) — How AI Sees Images in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian st…
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Convolutional Neural Networks (CNN) — How AI Sees Images in AI & ML
Convolutional Neural Networks (CNNs) are specialised neural networks designed for processing grid-like data such as images. They automatically learn spatial hierarchies of features: edges → shapes → parts → objects.
Convolution operation: A small filter (kernel), typically 3×3, slides across the image computing dot products. Each filter detects a specific pattern (horizontal edge, vertical edge, colour gradient). With many filters, the network learns many feature detectors.
Pooling: After convolution, pooling (usually max pooling) reduces spatial dimensions by taking the maximum in each region. This makes the representation smaller, faster, and spatially invariant (works even if the feature is slightly shifted).
Architecture: Input → [Conv + ReLU + Pool] × N → Flatten → Dense → Output. Famous architectures: LeNet (1998, handwriting), AlexNet (2012, ImageNet), VGG, ResNet, EfficientNet. ResNet (2015) introduced skip connections enabling 100+ layer networks.
Convolutional Neural Networks (CNN) — How AI Sees Images — Syntax
# CNN in PyTorch (conceptual): # import torch.nn as nn # model = nn.Sequential( # nn.Conv2d(in_ch=1, out_ch=32, kernel_size=3, padding=1), # nn.ReLU(), # nn.MaxPool2d(kernel_size=2), # nn.Flatten(), # nn.Linear(32*14*14, 10) # 10 classes # ) # # In keras: # Conv2D(32, (3,3), activation='relu') # MaxPooling2D(2,2)
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