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Python 3
CNN Architectures

CNN Architectures

CNN Architectures

CNN Architectures

As convolutional neural networks became more widely used, researchers began designing specific architectures, meaning particular arrangements of layers, that consistently performed well on image-related tasks. These architectures are essentially proven blueprints, refined over years of experimentation, that solve common problems like training stability, computational efficiency, and accuracy on large datasets.

Instead, they use one of these established architectures, sometimes with a pre-trained version, as a starting point.

LeNet and AlexNet

LeNet	AlexNet
Developed in the late 1990s.	Introduced several years later as a breakthrough CNN architecture.
One of the earliest practical CNN architectures.	Marked a major turning point for deep learning.
Originally designed for recognizing handwritten digits on bank checks.	Achieved outstanding performance in a major image recognition competition.
Small and simple compared to modern CNNs.	Much deeper than LeNet.
Demonstrated that convolutional layers could learn visual patterns directly from raw pixel data.	Showed that GPU training and deeper networks could achieve significantly better results on complex image recognition tasks.
Laid the foundation for modern CNN architectures.	Sparked renewed interest and investment in deep learning research.

VGGNet and ResNet

VGGNet	ResNet
Known for its simple and consistent architecture.	Introduced skip connections to solve problems in very deep networks.
Uses small, uniform filters stacked repeatedly across a deep network.	Skip connections allow information to bypass certain layers and flow more directly through the network.
Easy to understand and modify.	Makes it possible to train very deep networks, sometimes with over 100 layers.
Commonly used for transfer learning, where pre-trained layers are reused for new tasks.	Overcomes the training difficulties that affected earlier deep architectures.

GoogLeNet and MobileNet

GoogLeNet (Inception)	MobileNet
Introduced the Inception module.	Designed for efficient performance on mobile and embedded devices.
Uses multiple filter sizes in parallel within the same layer.	Uses a technique that breaks standard convolutions into smaller, lightweight operations.
Captures patterns at different scales simultaneously.	Significantly reduces the number of computations while maintaining good accuracy.
Improves accuracy while keeping computational cost manageable.	Ideal for smartphones, mobile apps, and embedded systems where computing resources are limited.

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CNN Architectures

Contents

CNN Architectures

LeNet and AlexNet

VGGNet and ResNet

GoogLeNet and MobileNet

Deep learning tutorial

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