A comprehensive conceptual and architectural journey through deep learning vision models, tracing the evolution from LeNet and AlexNet to ResNet, EfficientNet, and Vision Transformers. This course explains the design philosophies behind skip connections, bottlenecks, identity preservation, depth/width trade-offs, and attention mechanisms. Each chapter combines clear visuals, historical context, and side-by-side comparisons to reveal why architectures look the way they do and how they process information.

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15+ lessons
video
Not included
Free
Understand the evolution of deep learning vision architectures
Learn about LeNet, AlexNet, VGG, and GoogLeNet/Inception models
Explore skip connections and identity preservation in Highway Networks
Master ResNet, Wide ResNet, and DenseNet architectures
Basic understanding of neural networks
Familiarity with machine learning concepts
Basic knowledge of convolutional neural networks (helpful but not required)
Deep learning practitioners wanting to understand model architectures
Computer vision engineers and researchers
Machine learning students studying neural networks
AI developers interested in image processing
5 lessons
6 lessons
4 lessons
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