About OH Course Work Class Notes Lectures Recitations Assignments Docs & Tools Resources S21 F20
Course Work
Class Notes
Docs & Tools
S21 F20
11-785 Introduction to Deep Learning

Bulletin and Active Deadlines

Assignment Deadline Description Links
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.
Complete HW0 Feb. 7, 11:59PM EST - Homework 0 - Autolab, Handout
Watch All Recitations Feb. 1 This is a list of short recitations to prepare you for the assignments in the coming semester. List of Recitations
Sign up on Piazza ASAP - Piazza Sign Up Link
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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, 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.


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Pittsburgh Schedule (Eastern Time)

Lecture: Monday and Wednesday, 8:00 a.m. - 9:20 a.m.

Recitation: Friday, 8.00am-9.20am

Office hours:

We will be using OHQueue and Zoom links listed on Piazza to manage office hours. The tentative schedule will be updated soon.

Course Work

Quizzes      Grading will be based on weekly quizzes (24%), homeworks (51%) and a course project (25%).
Quizzes      There will be weekly quizzes.
  • Quiz 0 is mandatory; we will retain your best 12 out of the remaining 14 quizzes.
  • Quizzes will generally (but not always) be released on Friday and due 48 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 A (100%), B (80%), C (60%), D (40%) and F (0%) 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 51% of your total score. HW0 is worth 1%, while each of the subsequent four are worth 12.5%.
ProjectAll 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; 30% - Project Video; 5% - Responding to comments on Piazza; 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 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 here. Also, please follow the Piazza Etiquette when you use the piazza.

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.

Media Services/YouTube: Lecture and Reciation Recordings

CMU students who are not in the live lectures should watch the uploaded lectures at 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.

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.

Tentative Schedule of Lectures

Lecture Date Topics Slides and Video Additional Materials Quiz
0 -
  • Course Logistics
  • Learning Objectives
  • Grading
  • Deadlines
Slides (*.pdf)
Video (YT)
Quiz 0
1 Monday
Feb. 1
  • Introduction
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
The New Connectionism (1988)
On Alan Turing's Anticipation of Connectionism
Quiz 1
2 Wednesday
  • Neural Nets as Universal Approximators
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
Hornik et al. (1989)
Shannon (1949)
On the Bias-Variance Tradeoff
3 Monday
Feb.  8
  • Learning a Neural Net
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
Widrow and Lehr (1992)
Convergence of perceptron algorithm
Quiz 2
4 Wednesday
Feb. 10
  • Backpropogation
  • Calculus of backpropogation
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
Werbos (1990)
Rumelhart, Hinton and Williams (1986)
5 Monday
Feb. 15
  • Backpropogation, continued
  • Calculus of backpropogation, continued
Slides (*.pdf)
Video1 (YT)
Video1 (MT)
Video2 (YT)
Video2 (MT)
Chat (*.txt)
Werbos (1990)
Rumelhart, Hinton and Williams (1986)
Quiz 3
6 Wednesday
Feb. 17
  • Convergence issues
  • Loss Surfaces
  • Momentum
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
Backprop fails to separate, where perceptrons succeed, Brady et al. (1989)
Why Momentum Really Works
7 Monday
Feb. 22
  • Batch Size, SGD, Minibatch, second-order methods
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
Momentum, Polyak (1964)
Nestorov (1983)
Quiz 4
8 Wednesday
Feb. 24
  • Optimizers and Regularizers
    • Choosing a divergence (loss) function
    • Batch normalization
    • Dropout
Slides (*.pdf)
Video (YT)
Video1 (MT)
Video (YT)
Video2 (MT)
Chat (*.txt)
ADAGRAD, Duchi, Hazan and Singer (2011)
Adam: A method for stochastic optimization, Kingma and Ba (2014)
9 Monday
March 1
  • Shift invariance and Convolutional Neural Networks
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
Quiz 5
10 Wednesday
March 3
  • Models of vision, Convolutional Neural Networks
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
11 Monday
March 8
  • Learning in Convolutional Neural Networks
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
CNN Explainer Quiz 6
12 Wednesday
March 10
  • Learning in CNNs, transpose Convolution
See lecture 11 slides
Video (YT)
Video (MT)
Video P2 (MT)
Chat (*.txt)
13* Monday
March 15
  • Time Series and Recurrent Networks
Slides (*.pptx)
Video (YT)
Video (MT)
Chat (*.txt)
Falhman and Lebiere (1990)
13 Wednesday
March 17
  • Stability and Memory, LSTMs
Slides (*.pdf)
Video (YT)
Video (YT)
Video (MT)
Bidirectional Recurrent Neural Networks
14 Monday
March 22
  • Loss Functions in RNNs, Sequence Prediction
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
LSTM Quiz 8
15 Wednesday
March 24
  • Connectionist Temporal Classification
  • Sequence prediction
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
See recitation 2 on computing derivatives
16 Monday
March 29
  • Connectionist Temporal Classification (CTC)
  • Sequence To Sequence Prediction
Slides (*.pdf)
Video (YT)
Video (MT)
Chat (*.txt)
Labelling Unsegmented Sequence Data with Recurrent Neural Networks Quiz 9
17 Wednesday
March 31
  • Sequence To Sequence Methods
  • Attention
See lecture 16 slides
Video (YT)
Video (YT)
Video (MT)
Chat (*.txt)
18 Monday
Aprilr 5
  • No class
Slides (*.pdf)
Video (YT)
Video (MT)
Quiz 10
19 Wednesday
April 7
  • Representations and Autoencoders
Slides (*.pdf)
Video (YT)
Video (MT)
20 Monday
April 12
  • Variational Auto Encoders : EM and Variational Bounds
Slides (*.pdf)
Video (YT)
Video (MT)
Quiz 11
21 Wednesday
April 14
  • Variational Auto Encoders
Slides (*.pdf)
Video (YT)
Video (MT)
Tutorial on VAEs (Doersch)
Autoencoding variational Bayes (Kingma)
22* Monday
April 19
  • Generative Adversarial Networks, 1
Slides (*.pdf)
Video (YT)
Video (MT)
Quiz 12
22 Wednesday
April 21
  • Generative Adversarial Networks, 2
Video (MT)
23 Monday
April 26
  • Hopfield Nets
Video (MT)
Quiz 13
24 Wednesday
April 28
  • Hopfield Nets and Boltzmann Machines
Video (MT)
25 Monday
May 3
  • Wrap Up : A quick run through over everything we covered
Video (MT)
Quiz 14
27 Wednesday
May 5
  • Guest Lecture

Tentative Schedule of Recitations

Recitation Date Topics Materials Videos Instructor
0A Due Feb. 1 Object Oriented Programming Notebook (*.zip) Video (YT)
Shayeree Sarkar
0B Due Feb. 1 Fundamentals of NumPy and PyTorch Notebook (*.zip) Video (YT)
Nour Ali
0C Due Feb. 1 AWS Setup Handout Video (YT)
Vaidehi Joshi
0D Due Feb. 1 Introduction to Google Colab Handout Video (YT)
Haoxuan Zhu
0E Due Feb. 1 Debugging Notebook (*.zip) Video (YT)
Owen Wang
0F Due Feb. 1 Remote Notebooks Handout Video (YT)
Zhihao Wang


Date P1 HWs P2 HWs Bonus HWs Project Quizzes
Winter HW0p1 out
(see recitation 0s)
HW0p2 out
(see recitation 0s)
Quiz 0 (TBD)

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