update a3 to index

Author: lfangin

assignments/2020/assignment3.md

@@ -29,21 +29,20 @@ This assignment is due on **Wednesday, May 27 2020** at 11:59pm PDT.
 
 ### Goals
 
-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!
+In this assignment, you will implement recurrent neural networks and apply them to image captioning on the Microsoft COCO data. 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!
 
 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 architecture of recurrent neural networks (RNNs) and how they operate on sequences by sharing weights over time.
 - Understand and implement both Vanilla RNNs and Long-Short Term Memory (LSTM) networks.
-- Understand how to combine convolutional neural nets and recurrent nets to implement an image captioning system
+- Understand how to combine convolutional neural nets and recurrent nets to implement an image captioning system.
 - Explore various applications of image gradients, including saliency maps, fooling images, class visualizations.
 - Understand and implement techniques for image style transfer.
 - Understand how to train and implement a Generative Adversarial Network (GAN) to produce images that resemble samples from a dataset.
 
-
 ### Setup
 
-You should be able to use your setup from assignment 3.
+You should be able to use your setup from assignments 1 and 2.
 
 You can work on the assignment in one of two ways: **remotely** on Google Colaboratory or **locally** on your own machine.
 
@@ -82,21 +81,23 @@ Complete each notebook, then once you are done, go to the [submission instructio
 
 ### Q1: Image Captioning with Vanilla RNNs (29 points)
 
-The Jupyter notebook `RNN_Captioning.ipynb` will walk you through the implementation of an image captioning system on MS-COCO using vanilla recurrent networks.
+The notebook `RNN_Captioning.ipynb` will walk you through the implementation of an image captioning system on MS-COCO using vanilla recurrent networks.
 
 ### Q2: Image Captioning with LSTMs (30 points)
 
-The Jupyter notebook `LSTM_Captioning.ipynb` will walk you through the implementation of Long-Short Term Memory (LSTM) RNNs, and apply them to image captioning on MS-COCO.
+The notebook `LSTM_Captioning.ipynb` will walk you through the implementation of Long-Short Term Memory (LSTM) RNNs, and apply them to image captioning on MS-COCO.
 
 ### Q3: Network Visualization: Saliency maps, Class Visualization, and Fooling Images (15 points)
 
-The Jupyter notebooks `NetworkVisualization-TensorFlow.ipynb` /`NetworkVisualization-PyTorch.ipynb` will introduce the pretrained SqueezeNet model, compute gradients 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.
+The notebooks `NetworkVisualization-TensorFlow.ipynb`, and `NetworkVisualization-PyTorch.ipynb` will introduce the pretrained SqueezeNet model, compute gradients 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 (15 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 thenotebooks `StyleTransfer-TensorFlow.ipynb` or `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 (15 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 awarded if you complete both notebooks.
+
+In the notebooks `GANS-TensorFlow.ipynb` or `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.
 
 ### Submitting your work
 

index.html

@@ -24,6 +24,11 @@
         Assignment #2: Fully-Connected Nets, BatchNorm, Dropout, ConvNets, Tensorflow/Pytorch
       </a>
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+    <div class="materials-item">
+      <a href="assignments2020/assignment3/">
+        Assignment #3: Image Captioning with Vanilla RNNs and LSTMs, Neural Net Visualization, Style Transfer, Generative Adversarial Networks
+      </a>
+    </div>
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     <div class="materials-item">
       <a href="assignments2019/assignment2/">