Update assignment3.md

Author: jcjohnson

assignments/2017/assignment3.md

@@ -4,13 +4,13 @@ mathjax: true
 permalink: /assignments2017/assignment2/
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-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 use this model to implement Style Transfer. Finally, you will train a generative adversarial network to generate images that look like a training dataset!
+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!
 
 The goals of this assignment are as follows:
 
 - Understand the architecture of *recurrent neural networks (RNNs)* and how they operate on sequences by sharing weights over time
-- Understand the difference between vanilla RNNs and Long-Short Term Memory (LSTM) RNNs
-- Understand how to sample from an RNN at test-time
+- Understand and implement both Vanilla RNNs and Long-Short Term Memory (LSTM) RNNs
+- Understand how to sample from an RNN language model at test-time
 - Understand how to combine convolutional neural nets and recurrent nets to implement an image captioning system
 - Understand how a trained convolutional network can be used to compute gradients with respect to the input image
 - Implement and different applications of image gradients, including saliency maps, fooling images, class visualizations.
@@ -31,7 +31,7 @@ GPUs are **not required** for this assignment, but will help to speed up trainin
 
 Once you've got the cloud instance running, make sure to run the following line to enter the virtual environment that we prepared for you (you do **not** need to make your own virtual environment):
 
-```
+```bash
 source /home/cs231n/myVE35/bin/activate
 ```
 
@@ -113,8 +113,7 @@ with respect to images, and use them to produce saliency maps and fooling
 images. Please complete only one of the notebooks (TensorFlow or PyTorch). No extra credit will be awardeded if you complete both notebooks.
 
 ### Q4: Style Transfer (20 points)
-In the Jupyter notebooks `StyleTransfer-TensorFlow.ipynb`/`StyleTransfer-PyTorch.ipynb` you will learn how to create images with the content of one image but the style of another. . Please complete only one of the notebooks (TensorFlow or PyTorch). No extra credit will be awardeded if you complete both notebooks.
+In the Jupyter notebooks `StyleTransfer-TensorFlow.ipynb`/`StyleTransfer-PyTorch.ipynb` you will learn how to create images with the content of one image but the style of another. Please complete only one of the notebooks (TensorFlow or PyTorch). No extra credit will be awardeded if you complete both notebooks.
 
 ### Q5: Generative Adversarial Networks (20 points)
-
-In the Jupyter notebooks `GANs-TensorFlow.ipynb`/`GANs-PyTorch.ipynb` you will learn how to generate images that match a training dataset, and use these models to improve classifier performance when training on a large amount of unlabeled data and a small amount of labeled data. Please complete only one of the notebooks (TensorFlow or PyTorch). No extra credit will be awardeded if you complete both notebooks.
+In the Jupyter notebooks `GANs-TensorFlow.ipynb`/`GANs-PyTorch.ipynb` you will learn how to generate images that match a training dataset, and use these models to improve classifier performance when training on a large amount of unlabeled data and a small amount of labeled data. Please complete only one of the notebooks (TensorFlow or PyTorch). No extra credit will be awarded if you complete both notebooks.