http://cs231n.github.io/assignments2018/assignment1/
In this assignment you will practice putting together a simple image classification pipeline, based on the k-Nearest Neighbor or the SVM/Softmax classifier. The goals of this assignment are as follows:
- understand the basic Image Classification pipeline and the data-driven approach (train/predict stages)
- understand the train/val/test splits and the use of validation data for hyperparameter tuning.
- develop proficiency in writing efficient vectorized code with numpy
- implement and apply a k-Nearest Neighbor (kNN) classifier
- implement and apply a Multiclass Support Vector Machine (SVM) classifier
- implement and apply a Softmax classifier
- implement and apply a Two layer neural network classifier
- understand the differences and tradeoffs between these classifiers
- get a basic understanding of performance improvements from using higher-level representations than raw pixels (e.g. color histograms, Histogram of Gradient (HOG) features)