[cs231n]Linear classification: Support Vector Machine, Softmax
lecture webpapge
Key Words: parameteric approach, bias trick, hinge loss, cross-entropy loss, L2 regularization
lecture webpapge
Key Words: parameteric approach, bias trick, hinge loss, cross-entropy loss, L2 regularization
Lecture Website
Key words: L1/L2 distances, hyperparameter search, cross-validation, Nearest Neighbour
To be filled
Have not using Python for months. It is annoying to check the details every time when start picking up python. Therefore, I decide to write down some remiders (Most are just copied from the following tutorials).
Recently, I found this course and the material is interesting but elemental. Even though it does not contain video lectures, the slides and assignments are very well organized. Therefore, I decide to start following this course and get hands-on experience about Convolutional Neural Networks instead of just reading papers.
Given a binary tree, imagine yourself standing on the right side of it, return the values of the nodes you can see ordered from top to bottom.
Short Summary:
Use CNN feature and LSTM to learn to fix attention to a particular part of image while generating the corresponding words. Need to revisit this paper after a better understand of RNN for image caption analysis.
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