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“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 listed below.

Course description from student point of view

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.

Acknowledgments

Your Supporters

Instructor:

• Bhiksha Raj : bhiksha@cs.cmu.edu

TAs:

• Aishwarya Reganti: areganti@andrew.cmu.edu
• Amit Chahar: achahar@andrew.cmu.edu
• Ethan Xuanyue Yang: xuanyuey@andrew.cmu.edu
• Hanna Moazam: hmoazam@andrew.cmu.edu
• Hariharan Muralidharan: hmuralid@andrew.cmu.edu
• Kangrui Ruan (Darren): kangruir@andrew.cmu.edu
• Liwei Cai: liweicai@andrew.cmu.edu
• Pallavi Sharma: psharma2@andrew.cmu.edu
• Parth Shah: pvshah@andrew.cmu.edu
• Wendy Ebanks: wvelasqu@andrew.cmu.edu
• (Kigali) Aime Musangamfura: amusanga@andrew.cmu.edu
• (Kigali) Natnael Daba: ndaba@andrew.cmu.edu
• (Silicon Valley) Bonan Jin: bonanj@andrew.cmu.edu
• (Silicon Valley) Joseph Konan: jkonan@andrew.cmu.edu

Pittsburgh Schedule (Eastern Time)

Lecture: Monday and Wednesday, 9:00 a.m. - 10:20 a.m. @ DH A302

Recitation: Friday, 9.00am-10.20am @ DH A302

Kigali Schedule (Central Africa Time)

Lecture: Monday and Wednesday, 3:00 p.m. – 4:20 p.m. @ F305 DLR

• Aime Musangamfura: Monday and Wednesday, 6:00 p.m. – 8:00 p.m. @ TA Office B209
• Natnael Daba: Tuesday, 6:00 p.m. – 8:30 p.m. and Thursday 6:00 p.m. - 8:00 p.m. @ TA Office B209

Silicon Valley Schedule (Pacific Time)

• Bonan Jin: Wednesday, 1:00 p.m. – 3:00 p.m. @ B23, Room 107
• Joseph Konan: Monday-Sunday, 6:00 p.m. – 7:00 p.m. @ B23, Room 107

Prerequisites

1. We will be using one of several toolkits (the primary toolkit for recitations/instruction is PyTorch). The toolkits are largely programmed in Python. You will need to be able to program in at least one of these languages. Alternately, you will be responsible for finding and learning a toolkit that requires programming in a language you are comfortable with,
2. You will need familiarity with basic calculus (differentiation, chain rule), linear algebra and basic probability.

Units

11-785 is a graduate course worth 12 units. 11-485 is an undergraduate course worth 9 units.

Course Work

Grading

Grading will be based on weekly quizzes (24%), homeworks (51%) and a course project (25%).

Piazza: Discussion Board

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.

AutoLab: Software Engineering

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: Data Science

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.

YouTube: Lecture and Reciation Recordings

YouTube is where all lecture and recitation recordings will be uploaded. Links to individual lectures and recitations will also be posted below as they are uploaded. Videos marked “Old“ are not current, so please be aware of the video title.

CMU students can also access the videos Live from Media Services or Recorded from Media Services.

Books and Other Resources

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.

Academic Integrity

Tentative Schedule of Lectures

Documentation and Tools

Textbooks

Deep Learning By Ian Goodfellow, Yoshua Bengio, Aaron Courville Online book, 2017

Neural Networks and Deep Learning By Michael Nielsen Online book, 2016

Deep Learning with Python By J. Brownlee

Deep Learning Step by Step with Python: A Very Gentle Introduction to Deep Neural Networks for Practical Data Science By N. D. Lewis

Parallel Distributed Processing By Rumelhart and McClelland Out of print, 1986