http://liftothers.org/dokuwiki/ 2026-07-10T21:10:03+00:00 http://liftothers.org/dokuwiki/ http://liftothers.org/dokuwiki/lib/tpl/dokuwiki/images/favicon.ico text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2018:lab1&rev=1625096527&do=diff Objective: Get started with colab and python. Begin producing simple visualizations of data and images. ---------- Deliverable: For this lab, you will submit an ipython notebook via learningsuite. This notebook will have two parts: Part 1: Your notebook should generate a random image. We will run this notebook 5 times; it should generate 5 different, moderately complex images. Each image should be 512 x 288. Have fun with it! text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2018:lab2&rev=1625096527&do=diff Objective: Get started with pytorch. Begin to understand the basic boilerplate code of most pytorch programs. ---------- Deliverable: For this lab, you will submit an ipython notebook via learningsuite. This lab will be mostly boilerplate code, but you will be required to implement a few extras. text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2018:lab3&rev=1625096527&do=diff Objectives: * Build and train a deep conv net * Explore and implement various initialization techniques * Implement a parameterized module in Pytorch * Use a principled loss function Video Tutorial: <https://youtu.be/3TAuTcx-VCc> ---------- Deliverable: For this lab, you will submit an ipython notebook via learningsuite. This is where you build your first deep neural network! text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2018:lab4&rev=1625096527&do=diff BYU CS 501R - Deep Learning:Theory and Practice - Lab 4 Objective: * To build a dense prediction model * To begin reading current papers in DNN research ---------- Deliverable: For this lab, you will turn in a notebook that describes your efforts at creating a pytorch radiologist. Your final deliverable is a notebook that has (1) deep network, (2) cost function, (3) method of calculating accuracy, (4) an image that shows the dense prediction produced by your network on the text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2018:lab5&rev=1625096527&do=diff Objective: To explore an alternative use of DNNs by implementing the style transfer algorithm. To understand the importance of a complex loss function. To see how we can optimize not only over network parameters, but over other objects (such as images) as well. text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2018:lab6&rev=1625096527&do=diff Objective: To learn about recurrent neural networks, LSTMs, GRUs and Pytorch sequence-to-sequence capabilities. ---------- Deliverable: For this lab, you will need to implement the `char-rnn` model of Karpathy. You will train it on a text corpus that you're interested in, and then show samples from the model. text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2018:lab7&rev=1625096527&do=diff Objective: Gain exposure to state of the art attention mechanisms. ---------- Deliverable: For this lab, you will need to read, modify, and extend the Transformer model from this paper. You will use this new network build a Spanish to English translation system. text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2018:lab8&rev=1625096527&do=diff Objective: To learn about generative adversarial models. ---------- Deliverable: For this lab, you will need to implement a generative adversarial network (GAN). Specifically, we will be using the technique outlined in the paper Improved Training of Wasserstein GANs. You should turn in an iPython notebook that shows two plots. The first plot should be random samples from the final generator. The second should show interpolation between two faces by interpolating in text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2018:lab9&rev=1625096527&do=diff Objective: * To implement the Proximal Policy Optimization algorithm, and learn about the use of deep learning in the context of deep RL. ---------- Deliverable: For this lab, you will turn in a colab notebook that implements the proximal policy optimization (PPO) algorithm. You must provide tangible proof that your algorithm is working.