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Computer Vision
Chapters

1Foundations of Image Formation

2Low-Level Image Processing

3Feature Detection and Representation

4Classical Computer Vision: Geometry and Matching

5Machine Learning for Vision

6Deep Learning Foundations for Vision

Perceptrons and Multilayer PerceptronsBackpropagation and OptimizationActivation Functions and RegularizationConvolutional Neural Networks: ConceptsPooling, Padding, and StridesBatch Normalization and Normalization TechniquesOptimization Algorithms (SGD, Adam)Data Augmentation for VisionTransfer Learning and Pretrained ModelsTraining Best Practices and Debugging

7Advanced Deep Architectures and Vision Transformers

8Object Detection, Localization, and Recognition

Courses/Computer Vision/Deep Learning Foundations for Vision

Deep Learning Foundations for Vision

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Build core deep learning knowledge: neural networks, backpropagation, CNN basics, training practices and how they changed computer vision.

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Batch Normalization and Normalization Techniques

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