Deep Learning Vision Architectures Explained – Python Course on CNNs and Vision Transformers

This course is a conceptual and architectural journey through deep learning vision models, tracing the evolution from LeNet and AlexNet to ResNet, EfficientNet, and Vision Transformers.

The course explains the design philosophies behind skip connections, bottlenecks, identity preservation, depth/width trade-offs, and attention. 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.

Course developed by ‪@programmingoceanacademy‬

Course notes: https://www.programming-ocean.com/kno...

⭐️ Contents ⭐️
⌨️ (0:00:00) Welcoming and Introduction
⌨️ (0:01:44) What We'll Cover Broadly
⌨️ (0:05:34) LeNet Architecture Model
⌨️ (0:22:51) AlexNet Architecture Model
⌨️ (0:46:26) VGG Architecture Model
⌨️ (1:01:41) GoogLeNet / Inception Architecture Model
⌨️ (1:36:50) Highway Networks Architecture Model
⌨️ (2:00:45) Pathways of Information Preservation
⌨️ (2:18:03) ResNet Architecture Model
⌨️ (2:54:00) Wide ResNet Architecture Model
⌨️ (3:14:11) DenseNet Architecture Model
⌨️ (3:33:47) Xception
⌨️ (3:48:04) MobileNets
⌨️ (4:07:56) EfficientNets
⌨️ (4:24:32) Vision Transformers and The Ending

❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

🎉 Thanks to our Champion and Sponsor supporters:
👾 Drake Milly
👾 Ulises Moralez
👾 Goddard Tan
👾 David MG
👾 Matthew Springman
👾 Claudio
👾 Oscar R.
👾 jedi-or-sith
👾 Nattira Maneerat
👾 Justin Hual

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