http://liftothers.org/dokuwiki/ 2026-06-29T22:39:09+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_f2017:lab04&rev=1625096527&do=diff Objective The purpose of this lab is to help students understand the basic workflow for tensorflow. It’s composed of five parts: 1. Understand sessions, placeholders, and computation graphs; 2. Understand variables in tensorflow, and be able to train them with a simple delta rule; 3. Understand vectors, matrices and tensors, and be able to translate them into numpy arrays; 4. Understand data importation in python and tensorflow, be able to import a simple csv file and learn its hidden parameter… text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2017:lab5&rev=1625096527&do=diff Objective: To explore deeper networks, to leverage convolutions, and to explore Tensorboard. ---------- Deliverable: For this lab, you will need to perform three steps: * You need to implement the Deep MNIST for experts tutorial * You need to modify the tutorial code to deliver visualizations via Tensorboard. text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2017:lab5v2&rev=1625096527&do=diff Objective: To become more proficient in Tensorflow, to become more proficient in the construction of computation graphs and image classification, and to create and debug your first full DNN. ---------- Deliverable: There are two parts to this lab: text/html 2021-06-30T23:42:07+00:00 http://liftothers.org/dokuwiki/doku.php?id=cs501r_f2017:lab7&rev=1625096527&do=diff Objective: To learn about deconvolutions, variable sharing, trainable variables, and 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