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Announcements

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Zoran Kostic, PhD

Associate Professor of Prof. Practice, Electrical Engineering Dept., Columbia University.
zk2172@columbia.edu, instructor webpage, Office hours: Th. 11:30am-1pm, Shapiro 813, (for email communication - use subject: E4040 deep learning from student)

Course Assistants:

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

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Course Description

The course covers theoretical underpinnings, architecture and performance, datasets, and applications of neural networks and deep learning. The course will use Python coding language, TensorFlow deep learning framework and Google Cloud computational platform with GPUs (recommended). The main resource for the course is the book entitled "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press, http://www.deeplearningbook.org, 2016.

Students will contribute to the course by:

• Class activity
• Homeworks: Each student has to finish his/her own coding and question tasks in each assignment and upload his/her result under instrustions.
• Course projects: Students have to work on one project based on contemporary papers or original ideas in groups of no more than 3. Projects will have to be documented in code, a report, presentation/slides and (possibly) a website.
• Exam: Students will take a in-class exam covering the theory and deep learning architecture and design concepts. The exam will be after the break, closed book.
• Collecting the data and providing analysis for a self-contained data-collection project

There will be 4 homeworks in total. The first, homework 0, will help you ascertain the programming know-how needed to solve the following homeworks. All homeworks will be graded. If you have difficulties with homework 0, please carefully evaluate whether you will have enough time to brush up your programming knowledge by the end of the change of program period. The course requires theoretical background in probability and statistics and linear algebra. You are strongly advised to drop the class if you cannot satisfactorily finish questions of homework 0 by the add/drop date, or if you judge that the first lecture description of prerequsite linear algebra and probability/statistics may be overly demanding.

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Syllabus link

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Grading policy

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.

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Computational Environment and Prerequisites

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