|2018/10/03||New! Last announcement on the webpage. Course management and announcements have moved to courseworks.|
Shilin Hu, Zixiao Zhang, Yilin Lyu, Yong Yang, Zixi Huang and Zekun Gong(common email e4040TAs@columbia.edu).
OH: Mudd 1301 Time: Shilin & Yong: Tue 17:00 - 19:00; Zixiao & Yilin: Thu 13:00 - 15:00; Zixiao & Zekun: Fri 8:00 - 10:00
Students will contribute to the course by:
HW(40%), (one midterm) Exam(25%), Project(35%).
Late homeworks (assignments) Each student is entitled to 4 late days without penalty. For all homeworks together, a student can divide those four days in any fashion needed. Examples: (i) Homework 3 is late 4 days, in which case no other homework can be late for any amount of time; (ii) Homework 1 is late 1 day, homework 2 is late 2 days, in which case the student did not use all four late days. The unit of delay can not be divided into less than a full day (like hours). Requests for additional extensions will not be granted: if the budget of 4 days is blown, the student will be given 0 credit for homework(s) for which their submission is late.
There are many Python tutorials online for starters. Here we provide you with one tutorial link. For this course we recommend students to use python 3 grammar when finishing all programming assignments. Since on the cloud we only installed python 3, you may have problems if you code in python 2.7 on local machine and then want to run the same code on google cloud platform
Students will be running deep learning code on the Google Compute Engine (GCE) virtual machine (VM) instances, with access to GPU resources. This tutorial teaches how to setup a VM instance using the VM image which instructors have created for students.
For those who have a GPU-enabled computer or want to build a deep learning environment on the local machine/laptop, please see this guide on how to setup TensorFlow and PyTorch working environments.
The GCP VM instances which we use are Linux-based. If you haven't used Linux system before, this tutorial offers hands-on instruction to basic Linux commands that you will be using throughout the course.
TensorFlow is an open-source deep learning framework developed by Google. It is essential that you learn to program in TensorFlow for this course.
All of our homework distributions will be based on Bitbucket repositories, which can be manipulated by Git commands. If you don't know what Git is, it is strongly recommended that you read the first 3 chapters of this website.
Solutions to some common problems
ECBM E4040 Neural Networks and Deep Learning, 2018.