| Assignment | Deadline | Description | Links |
|---|---|---|---|
| Recitation 0A | Augut 31 | Fundamentals of Python | Notebook (*.zip) YouTube (url) |
| Recitation 0B | August 31 | Fundamentals of NumPy | Notebook (*.zip) YouTube (url) |
| Recitation 0C | August 31 | Fundamentals of PyTorch | Notebook (*.zip) YouTube (url) |
| Recitation 0D | August 31 | Fundamentals of AWS | Document (url) YouTube (url) |
| Recitation 0E | August 31 | Fundamentals of Google Colab | Document (url) YouTube (url) |
| Recitation 0F | August 31 | Debugging | Notebook (*.zip) YouTube (url) |
| Recitation 0G | August 31 | Remote Notebooks | Notebook (*.zip) Document (*.pdf) YouTube (url) |
| HW0 | August 31 | Fundamentals Homework | 0p1 Autolab, handout 0p2 Autolab, handout |
| Quiz 0 | September 6 | Fundamentals Quiz | Quiz |
| Information Forms | ASAP! | Forms about study groups and other important logistics |
Form 1 Form 2 |
“Deep Learning” systems, typified by deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. As a result, expertise in deep learning is fast changing from an esoteric desirable to a mandatory prerequisite in many advanced academic settings, and a large advantage in the industrial job market.
In this course we will learn about the basics of deep neural networks, and their applications to various AI tasks. By the end of the course, it is expected that students will have significant familiarity with the subject, and be able to apply Deep Learning to a variety of tasks. They will also be positioned to understand much of the current literature on the topic and extend their knowledge through further study.
If you are only interested in the lectures, you can watch them on the YouTube channel.
The course is well rounded in terms of concepts. It helps us understand the fundamentals of Deep Learning. The course starts off gradually with MLPs and it progresses into the more complicated concepts such as attention and sequence-to-sequence models. We get a complete hands on with PyTorch which is very important to implement Deep Learning models. As a student, you will learn the tools required for building Deep Learning models. The homeworks usually have 2 components which is Autolab and Kaggle. The Kaggle components allow us to explore multiple architectures and understand how to fine-tune and continuously improve models. The task for all the homeworks were similar and it was interesting to learn how the same task can be solved using multiple Deep Learning approaches. Overall, at the end of this course you will be confident enough to build and tune Deep Learning models.
Courses 11-785, 18-786, and 11-685 are equivalent 12-unit graduate courses, and have a final project. Course 11-485 is the undergraduate version worth 9 units, the only difference being that there is no final project.
Instructors:
TAs:
Lecture: Monday and Wednesday, 8:00 a.m. - 9:20 a.m.
Zoom Link: Meeting Link , Meeting ID: 403 746 7921
Recitation: Friday, 8.00am-9.20am BH A51
Lecture: Monday and Wednesday, 3:00 p.m. – 4:20 p.m. @ CMR C421
Office hours:| Policy | ||
| Breakdown | ||
| Quizzes | Grading will be based on weekly quizzes (24%), homeworks (51%) and a course project (25%). | |
| Quizzes | ||
| Quizzes |
There will be weekly quizzes.
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| Assignments | ||
| Assignments | There will be five assignments in all. Assignments will include autolab components, where you must complete designated tasks, and a kaggle component where you compete with your colleagues.
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| Project | ||
| Project | All students taking a graduate version of the course are required to do a course project. The project is worth 25% of your grade. These points are distributed as follows: 10% - Proposal; 15% - Midterm Report; 20% - Project Video; 15% - Project Video Follow-up; 40% - Paper peer review. | |
| Final grade | ||
| Final grade | The end-of-term grade is curved. Your overall grade will depend on your performance relative to your classmates. | |
| Pass/Fail | ||
| Pass/Fail | Students registered for pass/fail must complete all quizzes, HWs and if they are in the graduate course, the project. A grade equivalent to B- is required to pass the course. | |
| Auditing | ||
| Auditing | Auditors are not required to complete the course project, but must complete all quizzes and homeworks. We encourage doing a course project regardless. | |
| End Policy | ||
This semester we will be implementing study groups. It is highly recommended that you join a study group; see the forms on the bulletin.
Piazza is what we use for discussions. You should be automatically signed up if you're enrolled at the start of the semester. If not, please sign up. Also, please follow the Piazza Etiquette when you use the piazza.
AutoLab is what we use to test your understand of low-level concepts, such as engineering your own libraries, implementing important algorithms, and developing optimization methods from scratch.
Kaggle is where we test your understanding and ability to extend neural network architectures discussed in lecture. Similar to how AutoLab shows scores, Kaggle also shows scores, so don't feel intimidated -- we're here to help. We work on hot AI topics, like speech recognition, face recognition, and neural machine translation.
CMU students should also access the videos Live from Media Services or Recorded from Media Services in order to get attendance credit. Links to individual videos will be posted as they are uploaded.
YouTube is where non-CMU folks can view all lecture and recitation recordings. Videos marked “Old“ are not current, so please be aware of the video title.
The course will not follow a specific book, but will draw from a number of sources. We list relevant books at the end of this page. We will also put up links to relevant reading material for each class. Students are expected to familiarize themselves with the material before the class. The readings will sometimes be arcane and difficult to understand; if so, do not worry, we will present simpler explanations in class.
You can also find a nice catalog of models that are current in the literature here. We expect that you will be in a position to interpret, if not fully understand many of the architectures on the wiki and the catalog by the end of the course.
This is a selection of optional textbooks you may find useful
