CS230 Deep Learning (original) (raw)

Course Information

Course Staff

Course Assistants

Logistics

All course announcements take place through the CS230 Ed forum. Please make sure to join!

Class components

CS230 has the following components:

The flipped classroom format

CS230 follows a flipped-classroom format, every week you will have:

One module of the deeplearning.ai Deep Learning Specialization on Coursera includes:

Prerequisites

Students are expected to have the following background, and if they want, are invited to take the Workera technical assessments prior to the class to self-assess themselves prior to taking the class:

Grading

Here’s more information about the class grade:

Breakdown

Below is the breakdown of the class grade:

Note: For project meetings, every group must meet 3 times throughout the quarter:

  1. Before the project proposal deadline to discuss and validate the project idea. This can be with any TA.
  2. Before the milestone deadline, with your assigned project TA.
  3. Before the final report deadline, again with your assigned project TA.

Every student is allowed to and encouraged to meet more with the TAs, but only the 3 meetings above count towards the final participation grade.

Submitting Assignments

From the Coursera sessions (accessible from the invite you receive by email), you will be able to watch videos, solve quizzes and complete programming assignments. Each quiz and programming assignment can be submitted directly from the session and will be graded by our autograders.

You will submit your project deliverables on Gradescope. You should be added to Gradescope automatically by the end of the first week. If you are not added by the first week of the course, please make a private post on Ed.

Late assignments

Each student will have a total of ten free late (calendar) days to use for programming assignments, quizzes, project proposal and project milestone. Each late day is bound to only one assignment and is per student.

For example, if one quiz and one programming assignment are submitted 3 hours after the deadline, this results in 2 late days being used.

For example, if a group submitted their project proposal 23 hours after the deadline, this results in 1 late day being used per student.

Once these late days are exhausted, any assignments turned in late will be penalized 20% per late day. However, no assignment will be accepted more than three days after its due date, and late days cannot be used for the final project and final presentation. Each 24 hours or part thereof that a homework is late uses up one full late day. Also, note that if you submit an assignment multiple times, only the last one will be taken into account, in which case the number of late days will be calculated based on the last submission.

Students with Documented Disabilities

Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty. Unless the student has a temporary disability, Accommodation letters are issued for the entire academic year. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations. The OAE is located at 563 Salvatierra Walk (phone: 723-1066).

Honor code

We strongly encourage students to form study groups. Students may discuss and work on programming assignments and quizzes in groups. However, each student must write down the solutions independently, and without referring to written notes from the joint session. In other words, each student must understand the solution well enough in order to reconstruct it by him/herself. In addition, each student should submit his/her own code and mention anyone he/she collaborated with. It is also an honor code violation to copy, refer to, or look at written or code solutions from a previous year, including but not limited to: official solutions from a previous year, solutions posted online, and solutions you or someone else may have written up in a previous year. Furthermore, it is an honor code violation to post your assignment solutions online, such as on a public git repo.

The Stanford Honor Code

The Stanford Honor Code as it pertains to CS courses

Generative AI Policy: Each student is expected to submit their own work for assignments. You may use generative AI tools (i.e., Co-Pilot, ChatGPT) as you would use a human collaborator. You may not directly ask generative AI tools for answers or copy solutions, and you must acknowledge generative AI tools as collaborators. Using Generative AI tools to substantially complete an assignment or exam (e.g. by directly copying) is prohibited and will result in honor code violations. We will be doing our due diligence in reviewing assignments to enforce this policy. For more details: