added warnings for legacy assignments, removed old reference in setup page.

Author: Winnie Lin

assignments/2016/assignment1.md

@@ -3,6 +3,7 @@ layout: page
 mathjax: true
 permalink: /assignments2016/assignment1/
 ---
+**Note: this is the 2016 version of this assignment.**
 
 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:
 

assignments/2016/assignment2.md

@@ -3,6 +3,7 @@ layout: page
 mathjax: true
 permalink: assignments2016/assignment2/
 ---
+**Note: this is the 2016 version of this assignment.**
 
 In this assignment you will practice writing backpropagation code, and training
 Neural Networks and Convolutional Neural Networks. The goals of this assignment

assignments/2016/assignment3.md

@@ -3,6 +3,7 @@ layout: page
 mathjax: true
 permalink: assignments2016/assignment3/
 ---
+**Note: this is the 2016 version of this assignment.**
 
 In this assignment you will implement recurrent networks, and apply them to image captioning on Microsoft COCO. We will also introduce the TinyImageNet dataset, and use a pretrained model on this dataset to explore different applications of image gradients.
 

assignments/2017/assignment1.md

@@ -3,6 +3,7 @@ layout: page
 mathjax: true
 permalink: /assignments2017/assignment1/
 ---
+**Note: this is the 2017 version of this assignment.**
 
 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:
 

assignments/2017/assignment2.md

@@ -3,6 +3,7 @@ layout: page
 mathjax: true
 permalink: /assignments2017/assignment2/
 ---
+**Note: this is the 2017 version of this assignment.**
 
 In this assignment you will practice writing backpropagation code, and training
 Neural Networks and Convolutional Neural Networks. The goals of this assignment

assignments/2017/assignment3.md

@@ -3,6 +3,7 @@ layout: page
 mathjax: true
 permalink: /assignments2017/assignment3/
 ---
+**Note: this is the 2017 version of this assignment.**
 
 In this assignment you will implement recurrent networks, and apply them to image captioning on Microsoft COCO. You will also explore methods for visualizing the features of a pretrained model on ImageNet, and also this model to implement Style Transfer. Finally, you will train a generative adversarial network to generate images that look like a training dataset!
 

assignments/2018/assignment1.md

@@ -3,6 +3,7 @@ layout: page
 mathjax: true
 permalink: /assignments2018/assignment1/
 ---
+**Note: this is the 2018 version of this assignment.**
 
 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:
 

assignments/2018/assignment2.md

@@ -3,6 +3,7 @@ layout: page
 mathjax: true
 permalink: /assignments2018/assignment2/
 ---
+**Note: this is the 2018 version of this assignment.**
 
 In this assignment you will practice writing backpropagation code, and training
 Neural Networks and Convolutional Neural Networks. The goals of this assignment

assignments/2018/assignment3.md

@@ -3,6 +3,7 @@ layout: page
 mathjax: true
 permalink: /assignments2018/assignment3/
 ---
+**Note: this is the 2018 version of this assignment.**
 
 In this assignment you will implement recurrent networks, and apply them to image captioning on Microsoft COCO. You will also explore methods for visualizing the features of a pretrained model on ImageNet, and also this model to implement Style Transfer. Finally, you will train a Generative Adversarial Network to generate images that look like a training dataset!
 

setup.md

@@ -9,7 +9,7 @@ You can work on the assignment in one of two ways: locally on your own machine,
 
 ### Working remotely on Google Cloud (Recommended)
 
-**Note:** after following these instructions, make sure you go to **Download data** below (you can skip the **Working locally** section).
+**Note:** after following these instructions, you can skip the **Working locally** section.
 
 As part of this course, you can use Google Cloud for your assignments. We recommend this route for anyone who is having trouble with installation set-up, or if you would like to use better CPU/GPU resources than you may have locally. Please see the set-up tutorial [here](https://github.com/cs231n/gcloud/) for more details. :)