Update assignment3.md

assignments/2024/assignment3.md

@@ -43,23 +43,19 @@ The goals of this assignment are as follows:
 
 **You will use PyTorch for the majority of this homework.**
 
-### Q1: Network Visualization: Saliency Maps, Class Visualization, and Fooling Images
-
-The notebook `Network_Visualization.ipynb` will introduce the pretrained SqueezeNet model, compute gradients with respect to images, and use them to produce saliency maps and fooling images.
-
-### Q2: Image Captioning with Vanilla RNNs
+### Q1: Image Captioning with Vanilla RNNs
 
 The notebook `RNN_Captioning.ipynb` will walk you through the implementation of vanilla recurrent neural networks and apply them to image captioning on COCO.
 
-### Q3: Image Captioning with Transformers
+### Q2: Image Captioning with Transformers
 
 The notebook `Transformer_Captioning.ipynb` will walk you through the implementation of a Transformer model and apply it to image captioning on COCO.
 
-### Q4: Generative Adversarial Networks 
+### Q3: Generative Adversarial Networks 
 
 In the notebook `Generative_Adversarial_Networks.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. **When first opening the notebook, go to `Runtime > Change runtime type` and set `Hardware accelerator` to `GPU`.**
 
-### Q5: Self-Supervised Learning for Image Classification 
+### Q4: Self-Supervised Learning for Image Classification 
 
 In the notebook `Self_Supervised_Learning.ipynb`, you will learn how to leverage self-supervised pretraining to obtain better performance on image classification tasks. **When first opening the notebook, go to `Runtime > Change runtime type` and set `Hardware accelerator` to `GPU`.**