Manuka/release a2

_config.yml

@@ -19,6 +19,6 @@ kramdown:
   syntax_highlighter: rouge
 
 # links to homeworks
-hw_1_colab: https://cs231n.github.io/assignments/2022/assignment1_colab.zip
-hw_2_colab: https://cs231n.github.io/assignments/2022/assignment2_colab.zip
-hw_3_colab: https://cs231n.github.io/assignments/2022/assignment3_colab.zip
+hw_1_colab: https://cs231n.github.io/assignments/2023/assignment1_colab.zip
+hw_2_colab: https://cs231n.github.io/assignments/2023/assignment2_colab.zip
+hw_3_colab: https://cs231n.github.io/assignments/2023/assignment3_colab.zip

assignments/2023/assignment1.md

@@ -0,0 +1,85 @@
+---
+layout: page
+title: Assignment 1
+mathjax: true
+permalink: /assignments2023/assignment1/
+---
+
+<span style="color:red">This assignment is due on **Friday, April 21 2023** at 11:59pm PST.</span>
+
+Starter code containing Colab notebooks can be [downloaded here]({{site.hw_1_colab}}).
+
+- [Setup](#setup)
+- [Goals](#goals)
+- [Q1: k-Nearest Neighbor classifier](#q1-k-nearest-neighbor-classifier)
+- [Q2: Training a Support Vector Machine](#q2-training-a-support-vector-machine)
+- [Q3: Implement a Softmax classifier](#q3-implement-a-softmax-classifier)
+- [Q4: Two-Layer Neural Network](#q4-two-layer-neural-network)
+- [Q5: Higher Level Representations: Image Features](#q5-higher-level-representations-image-features)
+- [Submitting your work](#submitting-your-work)
+
+### Setup
+
+Please familiarize yourself with the [recommended workflow]({{site.baseurl}}/setup-instructions/#working-remotely-on-google-colaboratory) before starting the assignment. You should also watch the Colab walkthrough tutorial below.
+
+<iframe style="display: block; margin: auto;" width="560" height="315" src="https://www.youtube.com/embed/DsGd2e9JNH4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+**Note**. Ensure you are periodically saving your notebook (`File -> Save`) so that you don't lose your progress if you step away from the assignment and the Colab VM disconnects.
+
+Once you have completed all Colab notebooks **except `collect_submission.ipynb`**, proceed to the [submission instructions](#submitting-your-work).
+
+### Goals
+
+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:
+
+- Understand the basic **Image Classification pipeline** and the data-driven approach (train/predict stages).
+- Understand the train/val/test **splits** and the use of validation data for **hyperparameter tuning**.
+- Develop proficiency in writing efficient **vectorized** code with numpy.
+- Implement and apply a k-Nearest Neighbor (**kNN**) classifier.
+- Implement and apply a Multiclass Support Vector Machine (**SVM**) classifier.
+- Implement and apply a **Softmax** classifier.
+- Implement and apply a **Two layer neural network** classifier.
+- Understand the differences and tradeoffs between these classifiers.
+- Get a basic understanding of performance improvements from using **higher-level representations** as opposed to raw pixels, e.g. color histograms, Histogram of Oriented Gradient (HOG) features, etc.
+
+### Q1: k-Nearest Neighbor classifier
+
+The notebook **knn.ipynb** will walk you through implementing the kNN classifier.
+
+### Q2: Training a Support Vector Machine
+
+The notebook **svm.ipynb** will walk you through implementing the SVM classifier.
+
+### Q3: Implement a Softmax classifier
+
+The notebook **softmax.ipynb** will walk you through implementing the Softmax classifier.
+
+### Q4: Two-Layer Neural Network
+
+The notebook **two\_layer\_net.ipynb** will walk you through the implementation of a two-layer neural network classifier.
+
+### Q5: Higher Level Representations: Image Features
+
+The notebook **features.ipynb** will examine the improvements gained by using higher-level representations
+as opposed to using raw pixel values.
+
+### Submitting your work
+
+**Important**. Please make sure that the submitted notebooks have been run and the cell outputs are visible.
+
+Once you have completed all notebooks and filled out the necessary code, you need to follow the below instructions to submit your work:
+
+**1.** Open `collect_submission.ipynb` in Colab and execute the notebook cells.
+
+This notebook/script will:
+
+* Generate a zip file of your code (`.py` and `.ipynb`) called `a1_code_submission.zip`.
+* Convert all notebooks into a single PDF file.
+
+If your submission for this step was successful, you should see the following display message:
+
+`### Done! Please submit a1_code_submission.zip and a1_inline_submission.pdf to Gradescope. ###`
+
+**2.** Submit the PDF and the zip file to [Gradescope](https://www.gradescope.com/courses/527613).
+
+Remember to download `a1_code_submission.zip` and `a1_inline_submission.pdf` locally before submitting to Gradescope.

assignments/2023/assignment2.md

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+---
+layout: page
+title: Assignment 2
+mathjax: true
+permalink: /assignments2022/assignment2/
+---
+
+<span style="color:red">This assignment is due on **Monday, May 08 2023** at 11:59pm PST.</span>
+
+Starter code containing Colab notebooks can be [downloaded here]({{site.hw_2_colab}}).
+
+- [Setup](#setup)
+- [Goals](#goals)
+- [Q1: Multi-Layer Fully Connected Neural Networks](#q1-multi-layer-fully-connected-neural-networks)
+- [Q2: Batch Normalization](#q2-batch-normalization)
+- [Q3: Dropout](#q3-dropout)
+- [Q4: Convolutional Neural Networks](#q4-convolutional-neural-networks)
+- [Q5: PyTorch on CIFAR-10](#q5-pytorch-on-cifar-10)
+- [Q6: Network Visualization: Saliency Maps, Class Visualization, and Fooling Images](#q6-network-visualization-saliency-maps-class-visualization-and-fooling-images)
+- [Submitting your work](#submitting-your-work)
+
+### Setup
+
+Please familiarize yourself with the [recommended workflow]({{site.baseurl}}/setup-instructions/#working-remotely-on-google-colaboratory) before starting the assignment. You should also watch the Colab walkthrough tutorial below.
+
+<iframe style="display: block; margin: auto;" width="560" height="315" src="https://www.youtube.com/embed/DsGd2e9JNH4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+**Note**. Ensure you are periodically saving your notebook (`File -> Save`) so that you don't lose your progress if you step away from the assignment and the Colab VM disconnects.
+
+While we don't officially support local development, we've added a <b>requirements.txt</b> file that you can use to setup a virtual env.
+
+Once you have completed all Colab notebooks **except `collect_submission.ipynb`**, proceed to the [submission instructions](#submitting-your-work).
+
+### Goals
+
+In this assignment you will practice writing backpropagation code, and training Neural Networks and Convolutional Neural Networks. The goals of this assignment are as follows:
+
+- Understand **Neural Networks** and how they are arranged in layered architectures.
+- Understand and be able to implement (vectorized) **backpropagation**.
+- Implement various **update rules** used to optimize Neural Networks.
+- Implement **Batch Normalization** and **Layer Normalization** for training deep networks.
+- Implement **Dropout** to regularize networks.
+- Understand the architecture of **Convolutional Neural Networks** and get practice with training them.
+- Gain experience with a major deep learning framework, such as **TensorFlow** or **PyTorch**.
+- Explore various applications of image gradients, including saliency maps, fooling images, class visualizations.
+
+### Q1: Multi-Layer Fully Connected Neural Networks
+
+The notebook `FullyConnectedNets.ipynb` will have you implement fully connected
+networks of arbitrary depth. To optimize these models you will implement several
+popular update rules.
+
+### Q2: Batch Normalization
+
+In notebook `BatchNormalization.ipynb` you will implement batch normalization, and use it to train deep fully connected networks.
+
+### Q3: Dropout
+
+The notebook `Dropout.ipynb` will help you implement dropout and explore its effects on model generalization.
+
+### Q4: Convolutional Neural Networks
+
+In the notebook `ConvolutionalNetworks.ipynb` you will implement several new layers that are commonly used in convolutional networks.
+
+### Q5: PyTorch on CIFAR-10
+
+For this part, you will be working with PyTorch, a popular and powerful deep learning framework.
+
+Open up `PyTorch.ipynb`. There, you will learn how the framework works, culminating in training a convolutional network of your own design on CIFAR-10 to get the best performance you can.
+
+### Q6: 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.
+
+### Submitting your work
+
+**Important**. Please make sure that the submitted notebooks have been run and the cell outputs are visible.
+
+Once you have completed all notebooks and filled out the necessary code, you need to follow the below instructions to submit your work:
+
+**1.** Open `collect_submission.ipynb` in Colab and execute the notebook cells.
+
+This notebook/script will:
+
+* Generate a zip file of your code (`.py` and `.ipynb`) called `a2_code_submission.zip`.
+* Convert all notebooks into a single PDF file.
+
+If your submission for this step was successful, you should see the following display message:
+
+`### Done! Please submit a2_code_submission.zip and a2_inline_submission.pdf to Gradescope. ###`
+
+**2.** Submit the PDF and the zip file to [Gradescope](https://www.gradescope.com/courses/527613).
+
+Remember to download `a2_code_submission.zip` and `a2_inline_submission.pdf` locally before submitting to Gradescope.