🟢 What it covers?
A conceptual and architectural journey through computer vision models in deep learning, tracing the evolution from LeNet and AlexNet to ResNet, EfficientNet, and Vision Transformers.
The course explains the design principles behind skip connections, bottleneck blocks, identity preservation, depth/width trade-offs, and attention.
Each chapter combines clear illustrations, historical context, and side-by-side comparisons to show why architectures look the way they do and how they process information.
Available on YouTube
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🤖 Data Science, ML & Big Data with @DataXplore