Vision Encoders: How Machines Learned to See — From Convolutions to Vision Transformers
Understanding the two paradigms of visual representation learning — from local feature extraction with CNNs to global attention with Vision Transformers.
intermediate~3 hours3 notebooksConvolution OperationConvolutional Neural NetworksVision TransformersPatch EmbeddingsSelf-AttentionImage Classification
Curator of this Module
Dr. Rajat Dandekar
Course Instructor
Dr. Rajat Dandekar is a researcher and educator specializing in AI/ML, with a passion for making complex concepts accessible through intuitive explanations and hands-on learning.
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Learning Path
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