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F21 S21
11-785 Introduction to Deep Learning
Fall 2021

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.
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)

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.

Prerequisites

  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.

Units

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.

Acknowledgments

Your Supporters

Instructors:

TAs:

Pittsburgh Schedule (Eastern Time)

Lecture: Monday and Wednesday, 8:20 a.m. - 9:40 a.m.

Recitation: Friday, 8.20am-9.40am

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

Policy
Breakdown
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
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 48 hours later.
  • Quizzes are scored by the number of correct answers.
  • Quizzes will be worth 24% of your overall score.
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.
  • 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 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.
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; 30% - Project Video; 5% - Responding to comments on Piazza; 40% - Paper peer review.
  • 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 substititued with HW5. 11-685 Students may choose to do a Project instead of HW5. Either your Project OR HW5 will be graded.
Attendance
Attendance
  • 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
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

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 (Subject to Change)

Quiz
Lecture Date Topics Slides and Video Additional Materials Quiz
0 -
  • Course Logistics
  • Learning Objectives
  • Grading
  • Deadlines
No Quiz
1 Monday
Aug  30
  • Introduction
The New Connectionism (1988)
On Alan Turing's Anticipation of Connectionism
Quiz 1
2 Wednesday
Sept  1
  • Neural Nets as Universal Approximators
Hornik et al. (1989)
Shannon (1949)
On the Bias-Variance Tradeoff
- Monday
Sept 6
  • No class
Quiz 2
3 Wednesday
Sept  8
  • Learning a Neural Net
Widrow and Lehr (1992)
Convergence of perceptron algorithm
4 Monday
Sept 13
  • Backpropogation
  • Calculus of backpropogation
Werbos (1990)
Rumelhart, Hinton and Williams (1986)
Quiz 3
5 Wednesday
Sept  15
  • Backpropogation, continued
  • Calculus of backpropogation, continued
Werbos (1990)
Rumelhart, Hinton and Williams (1986)
6 Monday
Sept 20
  • Convergence issues
  • Loss Surfaces
  • Momentum
Backprop fails to separate, where perceptrons succeed, Brady et al. (1989)
Why Momentum Really Works
Quiz 4
7 Wednesday
Sept 22
  • Batch Size, SGD, Minibatch, second-order methods
Momentum, Polyak (1964)
Nestorov (1983)
8 Monday
Sept 27
  • Optimizers and Regularizers
    • Choosing a divergence (loss) function
    • Batch normalization
    • Dropout
ADAGRAD, Duchi, Hazan and Singer (2011)
Adam: A method for stochastic optimization, Kingma and Ba (2014)
Quiz 5
9 Wednesday
Sept 29
  • Shift invariance and Convolutional Neural Networks
10 Monday
Oct 4
  • Models of vision, Convolutional Neural Networks
Quiz 6
11 Wednesday
Oct 6
  • Learning in Convolutional Neural Networks
CNN Explainer
12 Monday
Oct 11
  • Learning in CNNs, transpose Convolution
Quiz 7
13 Wednesday
Oct 13
  • Time Series and Recurrent Networks
Fahlman and Lebiere (1990)
How to compute a derivative, extra help for HW3P1 (*.pptx)
14 Monday
Oct 18
  • Stability and Memory, LSTMs
Bidirectional Recurrent Neural Networks Quiz 8
15 Wednesday
Oct 20
  • Loss Functions in RNNs, Sequence Prediction
LSTM
16 Monday
Oct 25
  • Connectionist Temporal Classification
  • Sequence prediction
Quiz 9
17 Wednesday
Oct 27
  • Connectionist Temporal Classification (CTC)
  • Sequence To Sequence Prediction
Labelling Unsegmented Sequence Data with Recurrent Neural Networks
18 Monday
Nov 1
  • Sequence To Sequence Methods
  • Attention
Quiz 10
19 Wednesday
Nov 3
  • Representations and Autoencoders
20 Monday
Nov 8
  • Variational Auto Encoders : EM and Variational Bounds
Quiz 11
21 Wednesday
Nov 10
  • Variational Auto Encoders
Tutorial on VAEs (Doersch)
Autoencoding variational Bayes (Kingma)
22 Monday
Nov 15
  • Generative Adversarial Networks, 1
Quiz 12
23 Wednesday
Nov 17
  • Generative Adversarial Networks, 2
24 Monday
Nov 22
  • Hopfield Nets
Quiz 13
- Wednesday
Nov  24
  • No class
25 Monday
Nov 29
  • Hopfield Nets and Boltzmann Machines
Quiz 14
26 Wednesday
Dec 1
  • Wrap Up : A quick run through over everything we covered

Tentative Schedule of Recitations

Recitation Date Topics Materials Videos Instructor
0A Due Date: TBA Object Oriented Programming N/A Video (YT)
TBA
0B Due Date: TBA Fundamentals of NumPy and PyTorch N/A Video (YT)
TBA
0C Due Date: TBA AWS Setup N/A Video (YT)
TBA
0D Due Date: TBA Introduction to Google Colab N/A Video (YT)
TBA
0E Due Date: TBA Debugging N/A Video (YT)
TBA
0F Due Date: TBA Remote Notebooks N/A Video (YT)
TBA

Assignments and Quizzes

∑ Ongoing, ∏ Upcoming

Assignment Released Due Material / Links
HW0p1 Summer Break TBA TBA
HW0p2 Summer Break TBA TBA

Documentation and Tools

Textbooks

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