11-785 Introduction to Deep Learning
Fall 2019

Bulletin and Active Deadlines

Assignment Deadline Description Links
Homework 0 (hw0) September 2, 2019 Basics of Python, NumPy, and PyTorch Handout (*.tar.gz)

“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


Lecture: Monday and Wednesday, 9.00am-10.20am

Location: TBD

Recitation: Friday, 9.00am-10.20am

Location: TBD

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 Fall 2019 edition are tagged “F19”. 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
1 August 26
  • Introduction to deep learning
  • Course logistics
  • History and cognitive basis of neural computation.
  • The perceptron / multi-layer perceptron

2 August 28
  • The neural net as a universal approximator
3 September 2
  • Training a neural network
  • Perceptron learning rule
  • Empirical Risk Minimization
  • Optimization by gradient descent
4 September 4
  • Back propagation
  • Calculus of back propagation
5 September 9
  • Convergence in neural networks
  • Rates of convergence
  • Loss surfaces
  • Learning rates, and optimization methods
  • RMSProp, Adagrad, Momentum
6 September 11
  • Stochastic gradient descent
  • Optimization
  • Acceleration
  • Overfitting and regularization
  • Tricks of the trade:
    • Choosing a divergence (loss) function
    • Batch normalization
    • Dropout
7 September 16
  • Optimization
  • Generalization
8 September 18
  • Convolutional Neural Networks (CNNs)
  • Weights as templates
  • Translation invariance
  • Training with shared parameters
  • Arriving at the convolutional model
9 September 23
  • Convolutional Neural Networks
  • Models of vision
  • Neocognitron
  • Mathematical details of CNNs

10 September 25
  • Backpropagation through CNNs
  • Increasing output map size
  • Transform invariance
  • Alexnet, Inception, VGG
11 September 30
  • Recurrent Neural Networks (RNNs)
  • Modeling series
  • Back propagation through time (BPTT)
  • Bidirectional RNNs
12 October 2
  • Stability
  • Exploding/vanishing gradients
  • Long Short-Term Memory Units (LSTMs) and variants
How to compute a derivative
13 October 7
    Cascade Correlation Nets by Scott Fahlman
14 October 9
    Continual Learning by Pulkit Agarwal
Superposition of many models into one
15 October 14
  • Sequence To Sequence Modeling
  • Connectionist Temporal Classification (CTC)
16 October 16
  • Connectionist Temporal Classification (CTC)
17 October 21
  • Attention Models

18 October 23
  • What do networks learn
  • Autoencoders and dimensionality reduction
19 October 28
  • Hopfield Networks
20 October 30
  • Boltzmann Machines
21 November 4
  • Restricted Boltzman Machines (RBMs)
  • Deep Boltzman Machines (DBMs)
22 November 6
  • Linear Generative Models
  • Factor analysis
  • EM

23 November 11
  • Generative Adversarial Networks (GANs) Part 1
  • Non-linear generators
24 November 13
  • Generative Adversarial Networks (GANs) Part 2
25 November 18
  • Variational autoencoders
26 November 20
  • Reinforcement Learning Part 1
  • Markov Process
27 November 25
  • Reinforcement Learning Part 2
  • Value and Policy Iterations
28 November 27
  • Reinforcement Learning Part 3
  • TD Learning
29 Decmeber 2
  • Q Learning
  • Deep Q Learning
30 December 4
  • 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 August 15 Python coding for the deep learning student TBD
0 - Part 2 August 15 Python coding for the deep learning student Notebook TBD
1 August 30 Amazon Web Services (AWS) Slides
Parth Shah, Kangrui Ruan
2 September 6 Your First Deep Learning Code Slides TBD
3 September 13 Efficient Deep Learning/Optimization Methods Slides Notebook TBD
4 September 20 Debugging and Visualization Slides Notebook TBD
5 September 27 Convolutional Neural Networks Slides Notebook TBD
6 October 4 CNNs: HW2 Slides
Notebook TBD
7 October 11 Recurrent Neural Networks Slides
Notebook TBD
8 October 18 RNN: CTC Slides Notebook TBD
9 October 25 Attention Slides Notebook TBD
10 November 1 Variation Auto Encoders Slides
11 November 8 Attention Slides Video TBD
12 November 15 GANs TBD
13 November 29 Reinforcement Learning TBD


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 none pdf
HW1 1 An Introduction to Neural Networks - pdf
pdf (summer version)
zip (summer version)
2 Frame level classification of speech Kaggle
zip (summer version)
HW2 1 CNN - pdf
pdf (summer version)
tar (summer version)
2 Face Classification/Verification via CNN pdf
Classification Kaggle
Verification Kaggle
pdf (summer version)
Kaggle (summer version)
HW3 1 GRU - pdf
2 Utterance to Phoneme Mapping pdf
Slack Kaggle
Code Submission Form
Hw4 1 Language Modeling using RNNs - pdf
2 Attention pdf
Project Template

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