The History of Deep Learning Vision Architectures
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.
Instructor
Beau Carnes
I'm a teacher and developer with freeCodeCamp.org. I run the freeCodeCamp.org YouTube channel.
Course details
5 hours
video
Not included
Free
What you'll learn
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
Prerequisites
Basic understanding of neural networks
Familiarity with machine learning concepts
Basic knowledge of convolutional neural networks (helpful but not required)
Who this course is for
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
Curriculum
Introduction and Early Architectures
5 lessons
Advanced CNN Architectures
6 lessons
Efficient Architectures and Transformers
4 lessons
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