11-785 Introduction to Deep Learning
Spring 2019

FALL 19 Webpage

Information to fall students: There have been questions about the comparison of 11-785 to 10-617, also named “Introduction to deep learning.” The two are not the same course. The conflicting names were an error, and based on content, 10-617/417 is now being renamed “Intermediate DL”. Ruslan and I recommend that students without significant prior experience of DL first take 785/485; on the other hand if you have prior experience and are looking for more of the theoretical background, you take 617. Taking 785 followed by 617 would, in fact, be a natural progression of courses in a curriculum.

“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.

You don't have to be a CMU student to follow the course

Petr Ermakov and Artem Trunov are mirroring the course at OpenDataScience (ODS.ai). The mirrored course follows the CMU course in its entirety, quizzes, homeworks, piazza, discussion boards and all, and runs roughtly 3 weeks behind the CMU schedule. . There are currently about 1300 students signed up for it. If you are interested in the full course experience, you too can sign up for it at this site.

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.

Click here to read what students say about the previous edition of the course

Instructor: Bhiksha Raj


* -- contingent on registration

Lecture: Monday and Wednesday, 9.00am-10.20am

Location: Baker Hall A51

Recitation: Friday, 9.00am-10.20am

Location: Baker Hall A51

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.


This course is worth 12 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.


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.

Discussion board: Piazza

We will use Piazza for discussions. Here is the link. You should be automatically signed up if you're enrolled at the start of the semester. If not, please sign up.

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.


Kaggle is a popular data science platform where visitors compete to produce the best model for learning or analyzing a data set.

For assignments you will be submitting your evaluation results to a Kaggle leaderboard.


All recitations and lectures will be recorded and uploaded to Youtube. Here is a link to the Youtube channel. Links to individual lectures and recitations will also be posted below as they are uploaded. All videos for the Spring 2019 edition are tagged “S19”. CMU students can also access the videos on Panopto from this link.

Academic Integrity

You are expected to comply with the University Policy on Academic Integrity and Plagiarism.
  • You are allowed to talk with / 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

Lecture Date Topics Lecture notes/Slides Additional readings, if any Quizzes/Assignments Shadow Instructor
0 -
  • Course logistics
Cody Smith
1 January 14
  • Introduction to deep learning
  • Course logistics
  • History and cognitive basis of neural computation.
  • The perceptron / multi-layer perceptron

Assignment 0 due on January 20.
2 January 18
  • The neural net as a universal approximator
3 January 23
  • Training a neural network
  • Perceptron learning rule
  • Empirical Risk Minimization
  • Optimization by gradient descent
Assignment 1 released on January 24. Cody Smith
4 January 28
  • Back propagation
  • Calculus of back propagation
Cody Smith
5 January 30
  • Convergence in neural networks
  • Rates of convergence
  • Loss surfaces
  • Learning rates, and optimization methods
  • RMSProp, Adagrad, Momentum
6 February 4
  • Stochastic gradient descent
  • Optimization
  • Acceleration
  • Overfitting and regularization
  • Tricks of the trade:
    • Choosing a divergence (loss) function
    • Batch normalization
    • Dropout
7 February 6
  • Optimization
  • Generalization
8 February 11
  • Convolutional Neural Networks (CNNs)
  • Weights as templates
  • Translation invariance
  • Training with shared parameters
  • Arriving at the convolutional model
Daanish Ali Khan, Hengrui Liu
9 February 13
  • Convolutional Neural Networks
  • Models of vision
  • Neocognitron
  • Mathematical details of CNNs

Assignment 1 due on February 16
Assignment 2 released on February 17.
Sarvesh D., Hengrui Liu
10 February 18
  • Backpropagation through CNNs
  • Increasing output map size
  • Transform invariance
  • Alexnet, Inception, VGG
Simral Chaudhary, Daanish Ali Khan
11 February 20
  • Recurrent Neural Networks (RNNs)
  • Modeling series
  • Back propagation through time (BPTT)
  • Bidirectional RNNs
Simral Chaudhary, Sarvesh D.
12 February 25
  • Stability
  • Exploding/vanishing gradients
  • Long Short-Term Memory Units (LSTMs) and variants
How to compute a derivative
13 February 27
    Cascade Correlation Nets by Scott Fahlman
14 March 4
    Continual Learning by Pulkit Agarwal
Superposition of many models into one
15 March 6
  • Sequence To Sequence Modeling
  • Connectionist Temporal Classification (CTC)
15 March 8
  • Connectionist Temporal Classification (CTC)
16 March 18
  • Attention Models

Assignment 2 due on March 10.
Assignment 3 released on March 10.
17 March 20
  • What do networks learn
  • Autoencoders and dimensionality reduction
18 March 25
  • Hopfield Networks
19 March 27
  • Boltzmann Machines
20 April 1
  • Restricted Boltzman Machines (RBMs)
  • Deep Boltzman Machines (DBMs)
Assignment 3 due on March 31.
21 April 3
  • Linear Generative Models
  • Factor analysis
  • EM

Assignment 4 released on April 1.
22 April 8
  • Generative Adversarial Networks (GANs) Part 1
  • Non-linear generators
23 April 10
  • Generative Adversarial Networks (GANs) Part 2
24 April 15
  • Variational autoencoders
25 April 17
  • Reinforcement Learning Part 1
  • Markov Process
26 April 22
  • Reinforcement Learning Part 2
  • Value and Policy Iterations
27 April 24
  • Reinforcement Learning Part 3
  • TD Learning
28 April 29
  • Q Learning
  • Deep Q Learning
Assignments 4 due on April 28.
29 May 1
  • Guest lecture on biological models of cognition by Mike Tarr

Tentative Schedule of Recitations (Note: dates may shift)

Recitation Date Topics Lecture notes/Slides Notebook Videos Instructor
0 - Part 1 January 2 Python coding for the deep learning student Notebook Part 1 video
Simral Chaudhary,
Sarvesh D.
0 - Part 2 January 2 Python coding for the deep learning student Notebook Part 2 video Simral Chaudhary,
Sarvesh D.
1 January 16 Amazon Web Services (AWS) Slides
Video David Bick, Cody Smith
2 January 25 Your First Deep Learning Code Slides Video Alex Litzenberger, Daanish Ali Khan
3 February 1 Efficient Deep Learning/Optimization Methods Slides Notebook Video Kai Hu, Cody Smith, Josh Movenzadeh
4 February 8 Debugging and Visualization Slides Notebook Video Raphael Olivier, Sarvesh D.
5 February 15 Convolutional Neural Networks Slides Notebook Video Simral Chaudhary, Hengrui Liu, William Hu
6 February 22 CNNs: HW2 Slides
Notebook Video Hira Dhamyal, Hengrui Liu, Sarvesh D.
7 March 1 Recurrent Neural Networks Slides
Notebook Video Daanish Mir, Jakob Cassiman, David Bick
8 March 8 RNN: CTC Slides - p1
Slides - p2
Notebook Video Alex Litzenberger, William Hu
9 March 22 Attention Slides Notebook Video Daanish Mir, Jakob Cassiman, Simral C.
10 March 29 Variation Auto Encoders Slide1
Video Raphael Olivier, Shaden Shaar, William Hu
11 April 5 Attention Slides Video David, Josh
12 April 19 GANs Slides Video Hira, Simral, William
13 April 26 Reinforcement Learning Slides Video Alex, Josh


Most homeworks require submissions to autolab. If you are an autolab novice here is an “autolab for dummies” document to help you.

Number Part Topics Release date Early-submission deadline On-time deadline Links
HW0 - Python coding for DL 2 Jan none 20 Jan pdf
HW1 1 An Introduction to Neural Networks 24 Jan - 16 Feb pdf
2 Frame level classification of speech 24 Jan 3 Feb 16 Feb Kaggle
HW2 1 CNN 17 Feb - 10 March pdf
2 Face Classification/Verification via CNN 17 Feb 1 March 10 March pdf
Classification Kaggle
Verification Kaggle
Class. Slack Kaggle
Veri. Slack Kaggle
Class. Late Kaggle
Veri. Late Kaggle
HW3 1 GRU 16 March - 31 March pdf
2 Utterance to Phoneme Mapping 11 March 21 March 31 March pdf
Slack Kaggle
Code Submission Form
Hw4 1 Language Modeling using RNNs 1 April - 28 April pdf
2 Attention 1 April 20 April 28 April pdf
Project Template Poster instructions
Poster session: Monday May 6th, 3-5pm, Tuesday May 7th 3-5pm, Location (both days): GHC 7107 Atrium.

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