Deep Learning Vision Architectures Explained – CNNs from LeNet to Vision Transformers

This people is simply a conceptual and architectural travel done heavy learning imagination models, tracing the improvement from LeNet and AlexNet to ResNet, EfficientNet, and Vision Transformers. The people explains the creation philosophies down skip connections, bottlenecks, personality preservation, depth/width trade-offs, and attention. Each section combines clear visuals, humanities context, and side-by-side comparisons to uncover why architectures look the measurement they do and really they process information. Course developed by @programmingoceanacademy Course notes: https://www.programming-ocean.com/knowledge-hub/cnn-architect-mind-ai-atlas.php ⭐️ 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 transmission comes from our friends astatine Scrimba – the coding level 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 -- Learn to codification for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles connected programming: https://freecodecamp.org/news