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AI theory, architecture, models
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Module 6
Convolutional Neural Networks for Computer Vision
1
Building Blocks of CNNs: Conv & Pooling from Scratch
Implement 2D convolution and pooling operations from scratch
2
Understanding Receptive Fields in CNNs
Analyze the concept of receptive fields in CNNs
3
Building a Basic CNN for Image Classification
Build a foundational CNN for an image classification task
4
Classic CNN Architectures: LeNet, VGG, and ResNet
Analyze and implement classic CNN architectures: LeNet, VGG, and ResNet (with residual connections)
5
Implementing Advanced Architectural Patterns
Implement advanced architectural patterns like Inception modules
6
Efficient Convolutions and MobileNet Architecture Design
Implement efficient convolutions (depthwise separable) and design MobileNet-style architectures
7
Dilated Convolutions and Squeeze-and-Excitation Networks
Apply dilated convolutions and squeeze-and-excitation networks for advanced feature extraction
Previous module
Deep Neural Network Fundamentals
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Advanced Computer Vision Applications