Machine Learning/CS231n
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[CS231n] 3. OptimizationMachine Learning/CS231n 2022. 4. 27. 15:17
Keyword : Stochastic Gradient Descent 1) Introduction Two key components in context of the image classification task: A (parameterized) score function mapping the raw image pixels to class scores (e.g. a linear function) A loss function that measured the quality of a particular set of parameters based on how well the induced scores agreed with the ground truth labels in the training data.(e.g. S..
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[CS231n] 2. Linear ClassificationMachine Learning/CS231n 2022. 4. 26. 18:49
Keywords : Support Vector Machine, Softmax parameteric approah, bias trick, hinge loss, cross-entropy loss, L2 regularization Intro of Linear Classification In the last section we introduced the problem of Image Classification. K-Nearest Neighbor (kNN) classifier - it has a number of disadvantages. → The classifier must remember all of the training data and store it for future comparisons with t..
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[CS231n] 1. Image ClassificationMachine Learning/CS231n 2021. 4. 28. 00:08
Keywords : Data-driven Approach, K-Nearest Neighbor, train/validation/test splits L1,L2 distances, hyperparameter search, cross-validation 1. Image Classification The task of assigning an input image one label from a fixed set of categories One of the core problems in Computer Vision 1) Example A single image and assigns probabilities to 4 labels, {cat, dog, hat, mug} The cat image is 248 pixels..