聊了這麼多理論，這次要開始實踐了。
今天先來看看第一次作業的內容，Stanford給出的題目在這裏。
Q1: k-Nearest Neighbor classifier (20 points)
The IPython Notebook knn.ipynb will walk you through implementing the kNN classifier.
Q2: Training a Support Vector Machine (25 points)
The IPython Notebook svm.ipynb will walk you through implementing the SVM classifier.
Q3: Implement a Softmax classifier (20 points)
The IPython Notebook softmax.ipynb will walk you through implementing the Softmax classifier.
Q4: Two-Layer Neural Network (25 points)
The IPython Notebook two_layer_net.ipynb will walk you through the implementation of a two-layer neural network classifier.
Q5: Higher Level Representations: Image Features (10 points)
The IPython Notebook features.ipynb will walk you through this exercise, in which you will examine the improvements gained by using higher-level representations as opposed to using raw pixel values.
Q6: Cool Bonus: Do something extra! (+10 points)
Implement, investigate or analyze something extra surrounding the topics in this assignment, and using the code you developed. For example, is there some other interesting question we could have asked? Is there any insightful visualization you can plot? Or anything fun to look at? Or maybe you can experiment with a spin on the loss function? If you try out something cool we’ll give you up to 10 extra points and may feature your results in the lecture.
更於2019.8.5，待結更