From fe9d0cb86af39e89ab19deaf20538eee27985b01 Mon Sep 17 00:00:00 2001 From: Sam Kwong Date: Fri, 16 Apr 2021 11:50:09 -0700 Subject: [PATCH] Update text --- assignments/2021/assignment2.md | 25 ++++++++++++------------- index.html | 6 +++--- 2 files changed, 15 insertions(+), 16 deletions(-) diff --git a/assignments/2021/assignment2.md b/assignments/2021/assignment2.md index b6864d9b..d8428fe1 100644 --- a/assignments/2021/assignment2.md +++ b/assignments/2021/assignment2.md @@ -11,11 +11,11 @@ Starter code containing Colab notebooks can be [downloaded here]({{site.hw_2_col - [Setup](#setup) - [Goals](#goals) -- [Q1: Fully-connected Neural Network](#q1-fully-connected-neural-network) +- [Q1: Multi-Layer Fully Connected Neural Networks](#q1-fully-connected-neural-network) - [Q2: Batch Normalization](#q2-batch-normalization) - [Q3: Dropout](#q3-dropout) -- [Q4: Convolutional Networks](#q4-convolutional-networks) -- [Q5: PyTorch / TensorFlow on CIFAR-10](#q5-pytorch--tensorflow-on-cifar-10) +- [Q4: Convolutional Neural Networks](#q4-convolutional-networks) +- [Q5: PyTorch/TensorFlow on CIFAR-10](#q5-pytorch--tensorflow-on-cifar-10) - [Submitting your work](#submitting-your-work) ### Setup @@ -42,28 +42,27 @@ In this assignment you will practice writing backpropagation code, and training - 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**. -### Q1: Fully-connected Neural Network +### Q1: Multi-Layer Fully Connected Neural Networks -The notebook `FullyConnectedNets.ipynb` will introduce you to our -modular layer design, and then use those layers to implement fully-connected +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. +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. +The notebook `Dropout.ipynb` will help you implement dropout and explore its effects on model generalization. -### Q4: Convolutional Networks +### Q4: Convolutional Neural Networks -In the IPython Notebook `ConvolutionalNetworks.ipynb` you will implement several new layers that are commonly used in convolutional networks. +In the notebook `ConvolutionalNetworks.ipynb` you will implement several new layers that are commonly used in convolutional networks. -### Q5: PyTorch / TensorFlow on CIFAR-10 +### Q5: PyTorch/TensorFlow on CIFAR-10 -For this last part, you will be working in either TensorFlow or PyTorch, two popular and powerful deep learning frameworks. **You only need to complete ONE of these two notebooks.** You do NOT need to do both, and we will _not_ be awarding extra credit to those who do. +For this last part, you will be working in either TensorFlow or PyTorch, two popular and powerful deep learning frameworks. **You only need to complete ONE of these two notebooks.** While you are welcome to explore both for your own learning, there will be no extra credit. Open up either `PyTorch.ipynb` or `TensorFlow.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. @@ -82,7 +81,7 @@ This notebook/script will: If your submission for this step was successful, you should see the following display message: -`### Done! Please submit a1.zip and the pdfs to Gradescope. ###` +`### Done! Please submit a2.zip and the pdfs to Gradescope. ###` **2.** Submit the PDF and the zip file to [Gradescope](https://www.gradescope.com/courses/257661). diff --git a/index.html b/index.html index 40d71232..76444903 100644 --- a/index.html +++ b/index.html @@ -15,16 +15,16 @@
Spring 2021 Assignments
- Assignment #1: Image Classification, kNN, SVM, Softmax, Fully-Connected Neural Network + Assignment #1: Image Classification, kNN, SVM, Softmax, Fully Connected Neural Network
- Assignment #2: Fully-Connected and Convolutional Nets, Batch Normalization, Dropout + Assignment #2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Frameworks