About OH Course Work Class Notes Schedule of Lectures Schedule of Recitations Assignments Docs & Tools Resources F22 S22
Course Work
Class Notes
Schedule of Lectures
Schedule of Recitations
Docs & Tools
F22 S22
11-785 Introduction to Deep Learning
Fall 2022

In-Person Venue: Giant Eagle Auditorium, Baker Hall

Bulletin and Active Deadlines

Assignment Deadline Description Links
HW0 TBA TBA Autolab, Writeup, Handouts - TBA
Project Gallery
Here's an example of a successful project from Fall 2020.
The team developed an AI Limmerick generator,
and compiled a book from the AI Poet's creations.
Project Report, Project Video, Book (Amazon)
This piece is performed by the Chinese Music Institute at
Peking University (PKU) together with PKU's Chinese orchestra.
This is an adaptation of Beethoven: Serenade in D major, Op.25 - 1.
Entrata (Allegro),for Chinese transverse flute (Dizi), clarinet and flute.

CoVID-19 Related Announcement

In the event that the course is moved online due to CoVID-19, we will continue to deliver lectures via zoom. In the event that an instructor is unable to deliver a lecture in person, we will broadcast that lecture over zoom or, in extreme situations, expect you to view pre-recorded lectures from prior semesters. You will be notified through Piazza should any of these eventualities arise.

The Course

“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.

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.


  1. We will be using Numpy and PyTorch in this class, so you will need to be able to program in python3.
  2. You will need familiarity with basic calculus (differentiation, chain rule), linear algebra and basic probability.


Courses 11-785 and 11-685 are equivalent 12-unit graduate courses, and have a final project and HW 5 respectively. Course 11-485 is the undergraduate version worth 9 units, the only difference being that there is no final project or HW 5.


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Pittsburgh Schedule (Eastern Time)

Lecture: Mondays and Wednesdays, from 8:35 AM to 9:55 AM EDT

Recitation: Fridays, from 8:35AM to 9:55 AM

Google Calendar: The Google Calendar below ideally contains all events and deadlines for student's convenience. Please feel free to add this calendar to your Google Calendar by clicking on the plus (+) button on the bottom right corner of the calendar below. Any adhoc changes to the schedule will be visible on the calendar first.

Office hours: We will be using OHQueue for zoom related Office hours, others would be in-person. The OH schedule is given below.

Day Time (Eastern Time) TA Zoom/In Person Venue

Course Work

Score Assignment      Grading will be based on weekly quizzes (24%), homeworks (50%) and a course project (25%). Note that 1% of your grade is assigned to Attendance.
Quizzes      There will be weekly quizzes.
  • We will retain your best 12 out of the remaining 14 quizzes.
  • Quizzes will generally (but not always) be released on Friday and due 72 hours later.
  • Quizzes are scored by the number of correct answers.
  • Quizzes will be worth 24% of your overall score.
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.
  • Autolab components are scored according to the number of correctly completed parts.
  • We will post performance cutoffs for HIGH (90%), MEDIUM (70%), LOW (50%), and VERY LOW (30%) for Kaggle competitions. Scores will be interpolated linearly between these cutoffs.
  • Assignments will have a “preliminary submission deadline”, an “on-time submission deadline” and a “late-submission deadline.”
    • Early submission deadline: You are required to make at least one submission to Kaggle by this deadline. People who miss this deadline will automatically lose 10% of subsequent marks they may get on the homework. This is intended to encourage students to begin working on their assignments early.
    • On-time deadline: People who submit by this deadline are eligible for up to five bonus points. These points will be computed by interpolation between the A cutoff and the highest performance obtained for the HW. The highest performance will get 105.
    • Late deadline: People who submit after the on-time deadline can still submit until the late deadline. There is a 10% penalty applied to your final score, for submitting late.
    • Slack days: Everyone gets up to 7 slack days, which they can distribute across all their homework P2s only. Once you use up your slack days you will fall into the late-submission category by default. Slack days are accumulated over all parts of all homeworks, except HW0, to which no slack applies.
    • Kaggle scoring: We will use max(max(on-time score), max(slack-day score), .0.9*max(late-submission score)) as your final score for the HW. If this happens to be a slack-days submission, slack days corresponding to the selected submission will be counted.
  • Assignments carry 50% of your total score. HW0 is not graded (but is mandatory), while each of the subsequent four are worth 12.5%.
  • A fifth HW, HW5, will be released later in the course and will have the same weight as a course project. Please see Project section below for more details.
  • 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: 20% - Midterm Report; 35% - Project Video; 5% - Responding to comments on Piazza; 40% - Project report.
  • Note that a Project is mandatory for 11-785/18-786 students. In the event of a catastrophe (remember Spring 2020), the Project may be substituted with HW5. 11-685 Students may choose to do a Project instead of HW5. Either your Project OR HW5 will be graded.
  • If you are in section A you are expected to attend in-person lectures. We will track attendance.
  • If you are in any of the other (out-of-timezone) sections, you may either watch the real-time zoom lectures or the recorded lectures on mediatech
    • If viewed on mediatech, the lectures of each week must be viewed before 8AM of the Monday following the following week (Otherwise, it doesn’t count)
  • At the end of the semester, we will select a random subset of 50% of the lectures and tabulate attendance
  • If you have attended at least 70% of these (randomly chosen) lectures, you get the attendance point
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 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 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

Study groups

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: 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 TBA. Also, please follow the Piazza Etiquette when you use the piazza.

AutoLab: Software Engineering

AutoLab TBA 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.

Media Services/YouTube: Lecture and Reciation Recordings

CMU students who are not in the live lectures should watch the uploaded lectures at Media Services - Link TBA in order to get attendance credit. Links to individual videos will be posted as they are uploaded.

Our YouTube Channel 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.

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

You are expected to comply with the University Policy on Academic Integrity and Plagiarism.
  • You are allowed to talk with and work with other students on homework assignments.
  • You can share ideas but not code. You should submit your own code.
Your course instructor reserves the right to determine an appropriate penalty based on the violation of academic dishonesty that occurs. Violations of the university policy can result in severe penalties including failing this course and possible expulsion from Carnegie Mellon University. If you have any questions about this policy and any work you are doing in the course, please feel free to contact your instructor for help.

Class Notes

A book containing class notes is being developed in tandem with this course; check it out.

Schedule of Lectures

You can watch the recorded lectures on Mediaservices.
Lecture Date Topics Slides and Video Additional Materials Quiz
0 -
  • Course Logistics
  • Learning Objectives
  • Grading
  • Deadlines
Quiz 0
1 Monday,
29 Aug
  • Introduction

Quiz 1
2 Wednesday,
31 Aug
  • Neural Nets As Universal Approximators
3 Friday,
2 Sep
  • The problem of learning, Empirical Risk Minimization
4 Wednesday,
7 Sep
  • Empirical risk minimization and gradient descent
  • Training the network: Setting up the problem
Quiz 2
5 Monday,
12 Sep
  • Backpropagation
  • Calculus of Backpropagation
Quiz 3
6 Wednesday,
14 Sep
  • Convergence issues
  • Loss Surfaces
  • Momentum
7 Monday,
19 Sep
  • Optimization
  • Batch Size, SGD, Mini-batch, second-order methods
Quiz 4
8 Wednesday,
21 Sep
  • Optimizers and Regularizers
    • Choosing a divergence (loss) function
    • Batch normalization
    • Dropout
9 Monday,
26 Sep
  • Shift invariance and Convolutional Neural Networks
Quiz 5
10 Wednesday,
28 Sep
  • Models of vision, Convolutional Neural Networks
11 Monday,
3 Oct
  • Learning in Convolutional Neural Networks
Quiz 6
12 Wednesday,
5 Oct
  • Learning in CNNs
  • Transpose Convolution
  • CNN Stories
13 Monday,
10 Oct
  • Time Series and Recurrent Networks
Quiz 7
14 Wednesday,
12 Oct
  • Stability and Memory, LSTMs
- Monday,
17 Oct
  • No Class - Spring Break
- Quiz 8
- Wednesday,
19 Oct
  • No Class - Spring Break
15 Monday,
24 Oct
  • Loss Functions in RNNs, Sequence Prediction
Quiz 9
16 Wednesday,
26 Oct
  • Connectionist Temporal Classification
  • Sequence prediction
17 Monday,
31 Oct
  • Connectionist Temporal Classification (CTC) - Blanks and Beam-search
  • Sequence To Sequence Prediction
Quiz 10
18 Wednesday,
2 Nov
  • Sequence To Sequence Methods
  • Attention
19 Monday,
7 Nov
  • Transformers and GNNs
Quiz 11
20 Wednesday,
9 Nov
  • Learning Representations
  • AutoEncoders
21 Monday,
14 Nov
  • Variational Auto Encoders
Quiz 12
22 Wednesday,
16 Nov
  • Generative Adversarial Networks, 1
23 Monday,
21 Nov
  • Generative Adversarial Networks, 2
Quiz 13
- Wednesday,
23 Nov
  • No Class - Thanksgiving
24 Monday,
28 Nov
  • Hofield Nets and Auto Associators
Quiz 14
25 Wednesday,
30 Nov
  • Boltzmann Machines
26 Monday,
5 Dec
  • Guest Lecture 1
No Quiz
27 Wednesday,
7 Dec
  • Guest Lecture 2

Schedule of Recitations

Recitation Date Topics Materials Videos Instructor
0A Monday, 15th Aug Python & OOP Fundamentals

Zishen Wen, Fuyu Tang
0B Monday, 15th Aug Fundamentals of NumPy

Pranav Karnani, Karanveer Singh
0C Monday, 15th Aug PyTorch Tensor Fundamentals

Ruoyu Hua, Soumya Empran
0D Monday, 15th Aug Dataset & DataLoaders Gunjan Sethi, Samiran Gode, Pranav Karnani
0E Monday, 15th Aug Introduction to Google Colab Abuzar Khan, Aditya Singh
0F Monday, 15th Aug Debugging, Monitoring Ameya Mahabaleshwarkar, Cedric Manouan, Oscar Joris Denys
0G Monday, 15th Aug AWS Fundamentals

Yashash Gaurav, George Saito
0H Monday, 15th Aug WandB

Moayad Elamin, Gunjan Sethi, Vishhvak Srinivasan
0I TBA What to do if you're struggling Vishhvak Srinivasan, Yue Jian
0J Monday, 15th Aug Data Preprocessing Samruddhi Pai, Abuzar Khan
1 TBA Your first MLP Code TBA
2 TBA Optimizing the Networks, Ensembles TBA
HW1 Bootcamp TBA How to get started with HW1 TBA
3 TBA Computing Derivatives & Autograd TBA
4 TBA Hyperparameters Tuning TBA
5 TBA CNN: Basics & Backprop TBA
HW2 Bootcamp TBA How to get started with HW2 TBA
6 TBA CNNs: Classification & Verification TBA
7 TBA Paper Writing Workshop TBA
8 TBA RNN Basics (Pre-recorded) TBA
9 TBA CTC, Beam Search TBA
HW3 Bootcamp TBA How to get started with HW3 TBA
10 TBA Attention, MT, LAS TBA
11 Apr 1, 2022 Transformers TBA
HW4 Bootcamp April 6, 2022 How to get started with HW4 TBA
12 TBA Generative Adversarial Networks (GANs) + HW5 Bootcamp TBA
13 TBA Graph Neural Networks TBA
14 Pre-recorded YOLO TBA


∑ Ongoing, ∏ Upcoming

Assignment Release Date Due Date Related Materials / Links

(see recitation 0s)

(see recitation 0s)
HW1P1 Friday, 2nd Sept Early Bonus: 9th Sept, 11:59PM
Final: 30th Sept, 11:59PM
HW1P2 Friday, 2nd Sept Early: 9th Sept, 11:59 PM
Final: 30th Sept, 11:59 PM
HW1 Bonus Friday, 30th Sept Final: 26th Oct, 11:59 PM
Project Proposal - Friday, 30th Sept
HW2P1 Friday, 30th Sept Early Bonus: 7th Oct, 11:59 PM
Final: 26th Oct, 11:59 PM
HW2P2 Friday, 30th Sept Early: 7th Oct, 11:59 PM
Final: 26th Oct, 11:59 PM
Project Midterm Report - Friday, 4th Nov
HW3P1 Friday, 26th Oct Early Bonus: 4th Nov, 11:59 PM
Final: 11th Nov, 11:59 PM
HW3P2 Friday, 26th Oct Early: 4th Nov, 11:59 PM
Final: 11th Nov, 11:59 PM
HW4P1 Friday, 11th Nov Early Bonus: 18th Nov, 11:59 PM
Final: 9th Dec, 11:59 PM
HW4P2 Friday, 11th Nov Early: 18th Nov, 11:59 PM
Final: 9th Dec, 11:59 PM
Final Project Video Presentation & Preiliminary Project Report - 5th Dec, 11:59 PM
Project Peer reviews TBA
Final Project Report Submission - 9th Dec, 11:59 PM

Documentation and Tools


This is a selection of optional textbooks you may find useful

Deep Learning
Dive Into Deep Learning By Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola PDF, 2020
Deep Learning
Deep Learning By Ian Goodfellow, Yoshua Bengio, Aaron Courville Online book, 2017
Neural Networks and Deep Learning
Neural Networks and Deep Learning By Michael Nielsen Online book, 2016