11-785 Introduction to Deep Learning
Fall 2019

Bulletin and Active Deadlines

Assignment Deadline Description Links
Homework 0 September 8th, 2019 Basics of NumPy, and PyTorch Handout (*.tar.gz)
Homework 1 part 1 September 28th, 2019 Engineering Automatic Differentiation Libraries Handout(*.targ.gz)
Homework 1 part 2 September 28th, 2019 Frame-Level Speech Classification Kaggle

“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.

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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

Office hours:

Kigali Schedule (Central Africa Time)

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

Office hours:

Silicon Valley Schedule (Pacific Time)

Office hours:


  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.


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

Course Work


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

Quizzes      There will be weekly quizzes.
  • There are 14 quizzes in all. We will retain your best 12 scores.
  • 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, B, C, D and F for Kaggle competitions. These will translate to scores of 100, 80, 60, 40 and 0 respectively. 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 homeworks. 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 are required to do a course project. The project is worth 25% of your 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 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

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

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.

Tentative Schedule of Lectures

Lecture Date Topics Lecture Slides Additional Readings (if any) Homework & Assignments
0 -
  • Course logistics
Slides (*.pdf)
YouTube (url)
Homework 0 Released
1 August 28
  • Introduction to deep learning
  • History and cognitive basis of neural computation.
  • The perceptron / multi-layer perceptron
Slides (*.pdf) Youtube (url)
2 August 30
  • The neural net as a universal approximator
Slides (*.pdf)
Youtube (url)
Hornik et al. (*.pdf)
Shannon (*.pdf)
Koiran and Sontag (*.pdf)
September 2
  • Labor Day, no class
3 September 4
  • Training a neural network
  • Perceptron learning rule
  • Empirical Risk Minimization
  • Optimization by gradient descent
Slides (*.pdf)
Youtube (url)
September 8 Homework 0 Due
Homework 1 Released
4 September 9
  • Back propagation
  • Calculus of back propogation
Slides (*.pdf)
Youtube (url)
5 September 11
  • Back propagation Continued
Slides(*.pdf) Youtube (url)
6 September 16
  • Cognitive and Brain Science
  • Neural Basis of Cognition
7 September 18
  • Convergence in neural networks
  • Rates of convergence
  • Loss surfaces
  • Learning rates, and optimization methods
  • RMSProp, Adagrad, Momentum
8 September 23
  • Stochastic gradient descent
  • Acceleration
  • Overfitting and regularization
  • Tricks of the trade:
    • Choosing a divergence (loss) function
    • Batch normalization
    • Dropout
9 September 25
  • Convolutional Neural Networks (CNNs)
  • Weights as templates
  • Translation invariance
  • Training with shared parameters
  • Arriving at the convlutional model
10 September 30
  • Models of vision
  • Neocognitron
  • Mathematical details of CNNs
  • Alexnet, Inception, VGG
11 October 2
  • "Recurrent Neural Networks (RNNs)
  • Modeling series
  • Back propogation through time
  • Bidirectional RNNs"
12 October 7
  • Stability
  • Exploding/vanishing gradients
  • Long Short-Term Memory Units (LSTMs) and variants
  • Resnets
13 October 9
  • Loss functions for recurrent networks
  • Sequence prediction
14 October 14
  • Sequence To Sequence Methods
  • Connectionist Temporal Classification (CTC)
15 October 16
  • Sequence-to-sequence models Attention models examples from speech and language
16 October 21
  • What to networks represent
  • Autoencoders and dimensionality reduction
  • Learning representations
17 October 23
  • Variational Autoencoders (VAEs)
18 October 28
  • Generative Adversarial Networks (GANs) Part 1
19 October 30
  • Generative Adversarial Networks (GANs) Part 2
20 November 4
  • Hopfield Networks
  • Boltzmann Machines
21 November 6
  • Training Hopfield Networks
  • Stochastic Hopfield Networks
22 November 11
  • Restricted Boltzman Machines
  • Deep Boltzman Machines
23 November 13
  • Reinforcement Learning 1
24 November 18
  • Reinforcement Learning 2
25 November 20
  • Reinforcement Learning 3
26 November 25
  • Reinforcement Learning 4
November 27
  • Thanksgiving break, no classes
27 December 2
  • Q Learning
  • Deep Q Learning
28 December 4
  • Newer models and trends
  • Review

Tentative Schedule of Recitations

Recitation Date Topics Notebook Videos Instructor
0 - Part A August 16 Fundamentals of Python Notebook (*.tar.gz)
YouTube (url)
0 - Part B August 17 Fundamentals of NumPy Notebook (*.tar.gz) YouTube (url) Joseph
0 - Part C August 17 Fundamentals of Jupyter Notebook Notebook (*.tar.gz) YouTube (url) Joseph
1 August 26 Amazon Web Service (AWS) and EC2 Notebook (*.tar.gz) YouTube (url) Kangrui, Parth, Wendy
2 September 6 Your First Deep Learning Code Notebook (*.tar.gz) Youtube (url) Pallavi, Wendy
3 September 13 Efficient Deep Learning and Optimization Methods Notebook (*.tar.gz) Youtube(url) Aishwarya, Bonan, Hanna
4 September 20 Debugging and Visualization Liwei, Natnael, Ryan
5 September 27 Convolutional Neural Networks Aishwarya, Bonan, Kangrui
6 October 4 Convolutional Neural Networks (CNNs) and HW2 Bonan, Hanna, Parth
7 October 11 Recurrent Neural Networks (RNNs) Kangrui, Natnael
8 October 18 Connectionist Temporal Classification (CTC) in Recurrent Neural Networks (RNNs) Liwei, Natnael, Pallavi
9 October 25 Attention Mechanisms and Memory Networks Ethan, Pallavi
10 November 1 Variational Autoencoders Ethan
11 November 8 Generative Adversarial Networks (GANs) Hari, Parth, Amit
12 November 15 Reinforcement Learning Hari, Parth, Amit
13 November 29 Boltzmann Machines Ryan

Homework Schedule

Number Part Topics Release Date Early-submission Deadline On-time Deadline Links
HW0 August 12 September 8 Handout (*.tar.gz)
HW1 P1 Engineering Automatic Differentiation Libraries Sunday, Sept. 9th, 2019 Wednesday, Sept. 18th, 2019 Saturday, Sept. 28th, 2019 Handout(*.targ.gz)
P2 Frame-level Speech Classification Sunday, Sept. 9th, 2019 Wednesday, Sept. 18th, 2019 Saturday Sept. 28th, 2019 Kaggle
HW2 P1 Convolutional Neural Networks Sunday Sept. 29th, 2019 Wednesday, October 9th, 2019 Saturday, October 19th, 2019
P2 Face Recognition: Classification and Verification Sunday, Sept. 29th, 2019 Wednesday, October 9th, 2019 Saturday, October 19th, 2019
HW3 P1 Recurrent Neural Networks Sunday, October 20th, 2019 Wednesday, October 30th, 2019 Saturday, Nov. 9th, 2019
P2 Connectionist Temporal Classification Sunday, October 20th, 2019 Wednesday, October 30th, 2019 Saturday, Nov. 9th, 2019
HW4 P1 Word-Level Neural Language Models Sunday, Nov. 10th, 2019 Wednesday, Nov. 20th, 2019 Saturday, Dec. 7th, 2019
P2 Attention Mechanisms and Memory Networks Sunday, Nov. 10th, 2019 Wednesday, Nov. 20th, 2019 Saturday, Dec. 7th, 2019
Project Topics in the Theory and Application Deep Learning Monday, Sept. 30, 2019
Project Proposal
Thursday, Oct. 31, 2019
Midterm Report

Simplified Practice Assignments

Summer Practice Deadline Description Links
Homework 1 NA Multilayer Perceptrons Materials (*.tar.gz)
Homework 2 NA Basic Image Recognition Materials (*.tar.gz)
Homework 3 NA Basic Sequence Recognition Materials (*.tar.gz)
Homework 4 NA Basic Neural Language Translation Materials (*.tar.gz)

Documentation and Tools


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
Deep Learning with Python
Deep Learning with Python By J. Brownlee
Parallel Distributed Processing
Parallel Distributed Processing By Rumelhart and McClelland Out of print, 1986