About OH Course Work Class Notes Lectures Recitations Assignments Docs & Tools Resources F24 S24 F23
Course Work
Class Notes
Docs & Tools
S23 F22
11-785 Introduction to Deep Learning
Fall 2023
Class Streaming Link

In-Person Venue: Giant Eagle Auditorium, Baker Hall (A51)

Bulletin and Active Deadlines

Assignment Deadline Description Links
HW4P1 Early Submission: 16th Nov, 11:59 PM
Final Submission: 2nd Dec, 11:59 PM
Language Modeling Autolab
HW4P2 Early Submission: 16th Nov, 11:59 PM
Final Submission: 2nd Dec, 11:59 PM
Attention-based Speech Recognition Kaggle
Starter Notebook
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)
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.
Note for Enrolled Students: Please sign up for Piazza if you haven't done so. :-)

The Course

“Deep Learning” systems, typified by deep neural networks, are increasingly taking over all the AI tasks, ranging from language understanding, 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 and 11-685 are equivalent 12-unit graduate courses, and have a final project and HW 5 respectively. Course 11-485 is the undergraduate version worth 9 units, the only difference being that there is no final project or HW 5.


Your Supporters



Pittsburgh Schedule (Eastern Time)

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

Recitation: Friday, 8:00 a.m. - 9:20 a.m.

Event Calendar: The Google Calendar below ideally contains all events and deadlines for student's convenience (Deadlines for Assignments/HWs will be updates as and when they are released). Please feel free to add this calendar to your Google Calendar by clicking on the plus (+) button on the bottom right corner of the calendar below. Any adhoc changes to the schedule will be visible on the calendar first.

OH Calendar: The Google Calendar below contains the schedule for Office Hours. Please feel free to add this calendar to your Google Calendar by clicking on the plus (+) button on the bottom right corner of the calendar below. Any adhoc changes to the schedule, including extra OH, will be visible on the calendar first.

Office hours: This is the schedule for this semester. We will be using OHQueue(11-785) for both zoom and in-person Office hours. Please refer the OH calendar/ Piazza for updated information on OH hours.

Day Time (Eastern Time) TA Zoom/In Person
Monday 7:00AM to 8:00AM Muhammed Danso Zoom
11:00AM to 12:30PM Kelsey J. Harvey In-person
1:00PM to 2:00PM Miya Sylvester Zoom
4:30PM to 6:30PM Harini & Rucha & Pavitra & Dheeraj In-person (Wean 3110)
9:30PM to 10:30PM Meng Zhou Zoom
Tuesday 7:00AM to 8:00AM Schadrack Niyibizi In-person (Kigali)
8:00AM to 9:00AM Raghav & Sumesh In-person (GHC 5417)
11:00AM to 12:00PM Liangze "Josh" Li Zoom
2:30PM to 3:30PM Jeel & Pavitra In-person (GHC 5417)
5:00PM to 6:00PM Harshit Mehrotra In-person (GHC 5417)
6:00PM to 7:00PM Harshith Arun Kumar Zoom
Wednesday 7:00AM to 8:00AM Muhammed Danso In-person (Kigali)
12:30PM to 1:30PM Dheeraj Pai In-person (TCS349)
12:30PM to 1:30PM Harini & Rucha Zoom
4:00PM to 5:00PM Tony & Qin In-person (Wean 3110)
6:30 PM to 7:30 PM Sarthak Bisht In-person (GHC 5417)
Thursday 5:00AM to 6:00AM Yohannes Haile In-person (Kigali)
7:00AM to 8:00AM Schadrack Niyibizi Zoom
8:00AM to 9:00AM Raghav & Sumesh In-person (GHC 5417)
10:00AM to 11:00AM Emmanuel Ndayisaba In-person (Kigali)
11:00AM to 12:00PM Liangze "Josh" Li. Zoom
5:00PM to 6:00PM Jeel & Harshit In-person (GHC 5417)
6:00PM to 7:00PM Harshith Arun Kumar Zoom
Friday 9:00AM to 10:00AM Denis Musinguzi Zoom
9:00AM to 10:00AM Yohannes Haile Zoom
2:00PM to 3:00PM Miya Sylvester Zoom
4:30PM to 5:30PM Qin Wang Zoom
Saturday 9:00AM to 10:00AM Shikhar Agnihotri In-person (Wean 3119)
10:00AM to 11:00AM Jiaye "Tony" Zou Zoom
12:00PM to 2:00PM Kelsey J. Harvey Zoom
5:00PM to 6:00PM Sarthak Bisht In-person (GHC 5417)
Sunday 9:00AM to 10:00AM Denis Musinguzi In-person (GHC 5417)
10:00AM to 11:00AM Shikhar Agnihotri Zoom
5:00PM to 6.00PM Emmanuel Ndayisaba Zoom
9:00PM to 10:00PM Meng Zhou Zoom

Homework Hackathon: During 'Homework Hackathons', students will be assisted with homework by the course staff. It is recommended to come as study groups.
Location: Rashid Auditorium, located on the fourth floor of the Hillman Center (the part of the Gates and Hillman Centers closest to Forbes Avenue)
Time: Every Saturday, around 2-5pm

Course Work

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      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 There will be five assignments in all, plus the Peer Review assignment during the last week of the semester. 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 HIGH (90%), MEDIUM (70%), LOW (50%), and VERY LOW (30%) 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 10 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.
  • Bonus HWs will count towards the score of the correlating HWp1 assignment number. (For example, Bonus1 points go towards HW1p1.)
  • The Peer Review assignment is required of all students, 11-485/685/785. The task is for all students to review and grade 4-6 of the videos. It is to be completed over the last weekend, after classes finish (but before finals week). We will tell you which projects you have been assigned to review; each review should take around 15~20 minutes. Here is what we expect you to do for each review:
    • Watch the video carefully. As you watch the video, jot down some notes/concerns/questions that you might have.
    • Reference the initial report to clear up any confusion.
    • You must post at least one comment to the corresponding Piazza post of your reviewee. This comment must be a meaningful question or concern that demonstrates you have understood the material.
    • Finally, fill out the project review form carefully.
  • 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: 20% - Midterm Report; 35% - Project Video; 5% - Responding to comments on Piazza; 40% - Project report.
  • 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 substituted with HW5. 11-685 Students may choose to do a Project instead of HW5. Either your Project OR HW5 will be graded.
  • Important Information for project reports and video presentations (including midterm report rubric, final report rubric, video timeline, and video grading)
  • 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 must watch lectures live on zoom. Real-time viewing is mandatory unless you are in inconvenient time zones. Others are required to obtain specific permission to watch the pre-recorded lectures (on MediaServices).
  • If viewed on MediaServices, 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 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; Check piazza for further updates.

    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 TBA. 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.

    MediaServices/YouTube: Lecture and Reciation Recordings

    CMU students who are not in the live lectures should watch the uploaded lectures at MediaServices in order to get attendance credit. Links to individual videos will be posted as they are uploaded.

    Our YouTube Channel 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.

    Schedule of Lectures

    You can watch the recorded lectures on MediaServices.
    Lecture Date Topics Slides and Video Additional Materials Quiz
    0 -
    • Course Logistics
    • Learning Objectives
    • Grading
    • Deadlines
    Slides (*.pdf)
    Video (YT)

    1 Monday,
    28 Aug
    • Introduction
    Slides (*.pdf)
    Video (YT)
    The New Connectionism (1988)
    On Alan Turing's Anticipation of Connectionism
    McCullogh and Pitts paper
    Rosenblatt: The perceptron
    Bain: Mind and body
    Hebb: The Organization Of Behaviour
    Quiz 1
    2 Wednesday,
    30 Aug
    • Neural Nets As Universal Approximators
    Slides (*.pdf)
    Video (YT)
    Shannon (1949)
    Boolean Circuits
    On the Bias-Variance Tradeoff
    3 Monday,
    4 Sep
    • Training 1: Learning the Network
      (Mandatory to watch recording before Wednesday)
    Slides (*.pdf)
    Video (YT)
    Quiz 2
    4 Wednesday,
    6 Sep
    • Training 2: The problem of learning, Empirical Risk Minimization
    Slides (*.pdf)
    Video (YT)
    Widrow and Lehr (1992)
    Adaline and Madaline
    Convergence of perceptron algorithm
    Threshold Logic
    TC (Complexity)
    AC (Complexity)
    5 Monday,
    11 Sep
    • Training 3:
      • Empirical risk minimization and gradient descent
      • Training the network: Setting up the problem
      • Backpropagation
      • Calculus of Backpropagation
    Slides (*.pdf)
    Video (YT)
    Video, 5b (YT)
    Werbos (1990)
    Rumelhart, Hinton and Williams (1986)
    Quiz 3
    6 Wednesday,
    13 Sep
    • Training 4
      • Convergence issues
      • Loss Surfaces
      • Momentum
    Slides (*.pdf)
    Video (YT)
    Backprop fails to separate, where perceptrons succeed, Brady et al. (1989)
    Why Momentum Really Works
    7 Monday,
    18 Sep
    • Training 5
      • Optimization
      • Batch Size, SGD, Mini-batch, second-order methods
    Slides (*.pdf)
    Video (YT)
    Momentum, Polyak (1964)
    Nestorov (1983)
    Derivatives and Influences
    Quiz 4
    8 Wednesday,
    20 Sep
    • Training 6
      • Optimizers and Regularizers
        • Choosing a divergence (loss) function
        • Batch normalization
        • Dropout
    Slides (*.pdf)
    Video, 8a (YT) Video, 8b (YT)
    Derivatives and Influence Diagrams
    ADAGRAD, Duchi, Hazan and Singer (2011)
    Adam: A method for stochastic optimization, Kingma and Ba (2014)
    9 Monday,
    25 Sep
    • CNN 1: Shift Invariance
    Slides (*.pdf)
    Video (YT)
    Quiz 5
    10 Wednesday,
    27 Sep
    • CNN 2
    Slides (*.pdf)
    Video, 10a (YT)
    Video, 10b (YT)
    11 Monday,
    2 Oct
    • CNN 3
    Slides (*.pdf)
    Video (YT)
    Quiz 6
    12 Wednesday,
    4 Oct
    • CNN 4 - Newer Architectures
    Slides (*.pdf)
    Video (YT)
    13 Monday,
    9 Oct
    • RNN 1
    Slides (*.pdf)
    Video (YT)
    Quiz 7
    14 Wednesday,
    11 Oct
    • RNN 2
    Slides (*.pdf)
    Video (YT)
    How to compute a derivative, extra help for HW3P1 (*.pptx)
    - Monday,
    16 Oct
    • No Class - Fall Break
    - Quiz 8
    - Wednesday,
    18 Oct
    • No Class - Fall Break
    15 Monday,
    23 Oct
    • Sequence to sequence
    • Connectionist Temporal Classification (CTC)
    Slides (*.pdf)
    Video, 15a (YT)
    Video, 15b (YT)
    Quiz 9
    16 Wednesday,
    25 Oct
    • Connectionist Temporal Classification (CTC)
    Slides (*.pdf)
    Video (YT)
    Labelling Unsegmented Sequence Data with Recurrent Neural Networks
    17 Monday,
    30 Oct
    • Language modeling
    • Translation
    Slides (*.pdf)
    Video (YT)
    Quiz 10
    18 Wednesday,
    1 Nov
    • Attention Models
    • Transformers
    Slides (*.pdf)
    Video (YT)
    Attention Is All You Need
    The Annotated Transformer - Attention is All You Need paper, but annotated and coded in pytorch!
    19 Monday,
    6 Nov
    • Transformers
    • Newer Transformer Architectures
    • GNNs
    Slides (*.pdf)
    Video (YT)
    A comprehensive Survey on Graph Neural Networks Quiz 11
    20 Wednesday,
    8 Nov
    • Learning Representations
    • Autoencoders
    • Losses
    Slides (*.pdf)
    Video (YT)
    21 Monday,
    13 Nov
    • Variational Auto Encoders
    Slides (*.pdf)
    Video (pre-recorded) (YT)
    Tutorial on VAEs (Doersch)
    Autoencoding variational Bayes (Kingma)
    Quiz 12
    22 Wednesday,
    15 Nov
    • Variational Auto Encoders II
    Slides (*.pdf)
    Video (pre-recorded) (YT)
    23 Monday,
    20 Nov
    • Flow and Diffusion
    Slides (*.pdf)
    Video (YT)
    Quiz 13
    - Wednesday,
    22 Nov
    • No Class - Thanksgiving
    24 Monday,
    27 Nov
    • Generative Adversarial Networks 1
    Slides (*.pdf)
    Video (YT)
    Quiz 14
    25 Wednesday,
    29 Nov
    • Generative Adversarial Networks 2
    Slides (*.pdf)
    Video (YT)
    26 Monday,
    4 Dec
    • Hopfield Nets and Auto Associators
    Slides (*.pdf)
    Video (YT)
    No Quiz
    27 Wednesday,
    6 Dec
    • Guest Lecture: Pulkit Agrawal, MIT
    Video (YT)

    Schedule of Recitations

    ` `
    Recitation Date Topics Materials Videos Instructor
    0A Friday, 25th August Python & OOP Fundamentals

    Video (YT)

    Sumesh & Raghav
    0B Friday, 25th August OOP Fundamentals Notebook (*.zip)

    Video (YT)

    Aishwarya & Denis
    0C Friday, 25th August NumPy Fundamentals Notebook + Exercise (*.zip)

    Video (YT): 1, 2, 3

    Harini & Meng
    0D Friday, 25th August PyTorch Fundamentals Notebook + Slides + Exercise (*.zip) Video (YT)

    Jean & Sumesh
    0E Friday, 25th August Introduction to Google Colab Colab Notebook Video (YT)

    Rucha & Tony
    0F Friday, 25th August Google Cloud VM Setup Script Video (YT): 1, 2

    Meng & Harini
    0G Friday, 25th August AWS Video (YT)

    Josh & Yohannes
    0H Friday, 25th August Kaggle Video (YT)

    Denis & Shikhar
    0I Friday, 25th August Datasets Colab Notebook Video (YT) 1, 2

    Tony & Muhammed
    0J Friday, 25th August Dataloaders Notebook (*.zip) Video (YT)

    Raghav & Qin
    0K Friday, 25th August Data Preprocessing Colab Notebook
    1, 2, 3
    Video (YT) 1, 2, 3
    Qin & Tony
    0L Friday, 25th August Debugging Slides (*.zip) Video (YT) 1, 2, 3, 4
    Shikhar & Harshith
    0M Friday, 25th August What to do when struggling Video (YT)
    Miya & Aishwarya
    0N Friday, 25th August Cheating Video (YT)
    Dheeraj & Sarthak
    0O Friday, 25th August Workflow of HWs Notebook (*.zip) Video (YT)
    Jeel & Jean
    0P Friday, 25th August WandB Colab Notebook Video (YT) 1, 2
    Harshit & Jeel
    0Q Friday, 25th August To write a report Video (YT)
    David (Spring 2022)
    0R Friday, 25th August Flow of the project Video (YT)
    Pavitra & Rucha
    0S Friday, 25th August Git Video (YT)
    Josh & Harshit
    0T Friday, 25th August Losses Notebook (*.zip) Video (YT) Dheeraj & Harshith
    0U Friday, 25th August Block Processing Colab Notebook Video (YT) Vish & Muhammed
    1 Friday, 1st September Your first MLP Code Notebook
    Video (YT) Jiaye "Tony" Zou & Kelsey J. Harvey
    HW1 Bootcamp Thursday, 7th September How to get started with HW1 Slides (*.zip) Video (YT) Denis Musinguzi, Harshit Mehrotra, Liangze "Josh" Li, Pavitra Kadiyala, Qin Wang, Yohannes, Schadrack
    2 Friday, 8th September Optimizing the Networks, Hyperparameter Tuning, Ensembles Slides (*.pdf) Video (YT)
    Harshit Mehrotra & Liangze "Josh" Li
    Hackathon Saturday, 9th September HW1 Dheeraj Pai, Liangze "Josh" Li, Pavitra Kadiyala
    3 Friday, 15th September Computing Derivatives & Autograd Slides (*.pdf)
    Video (YT)
    Dheeraj Pai & Miya Sylvester
    Hackathon Saturday, 16th September HW1 Dheeraj Pai, Pavitra Kadiyala, R Raghav
    4 Friday, 22nd September Hyperparameter Tuning Methods, Normalizations Slides (*.pdf) Video (YT)
    Harini Subramanyan & Jeel Shah
    Hackathon Saturday, 23rd September HW1 Harshit Mehrotra & Jeel Shah
    HW2 Bootcamp Thursday, 28th September How to get started with HW2 Slides, P1 (*.pdf) Slides, P2 (*.pdf) Video (YT) Harini Subramanyan, Jeel Shah, Qin Wang, R Raghav, Rucha Manoj Kulkarni, Vish, Muhammad Danso, Schadrack Niyibizi
    5 Friday, 29th September CNN: Basics and Backprop Slides(*.pptx) Video (YT) R Raghav & Schadrack Niyibizi
    Hackathon Saturday, 30th September HW2 Miya Sylvester, Rucha Manoj Kulkarni, Schadrack Niyibizi
    6 Friday, 6th October CNNs: Classification, Verification Slides (*.pptx) Video (YT)
    Sumesh Kalambettu Suresh & Emmanuel Ndayisaba
    Hackathon Friday, 7th October HW2 Harini Subramanyan, Harshith Arun Kumar, Schadrack Niyibizi
    7 Friday, 13th October Paper Writing Workshop Slides (*.pdf) Video (YT)
    Pavitra Kadiyala & Dheeraj Pai
    Hackathon Saturday, 14th October HW2 Qin Wang, Harshith Kumar, R Raghav, Sumesh Kalambettu Suresh, Schadrack Niyibizi
    Hackathon Saturday, 21st October HW2 Schadrack Niyibizi
    HW3 Bootcamp Thursday, 26th October How to get started with HW3 Video (YT)
    Harshit Mehrotra, Qin Wang, Rucha Manoj Kulkarni, Shikhar Agnihotri Sumesh Kalambettu Suresh, Yohannes, Jiaye "Tony" Zou
    8 Friday, 27th October RNN Basics Slides (*.pdf)
    Notebook (*.zip)
    Video (YT)
    Meng Zhou & Shreyas
    Hackathon Saturday, 28th October HW3 Miya Sylvester, Rucha Manoj Kulkarni, Schadrack Niyibizi
    9 Friday, 3rd November CTC, Beam Search Notebook (.ipynb)
    Slides (.pptx)
    Video (YT)
    Qin Wang & Shikhar Agnihotri
    Hackathon Saturday, 4th November HW3 Qin Wang, Schadrack Niyibizi
    HW4 Bootcamp Thursday, 9th November How to get started with HW4 Slides (.pdf)
    Video (YT)
    Denis Musinguzi, Harini Subramanyan, Harshit Mehrotra, Liangze "Josh" Li, Meng Zhou, Shikhar Agnihotri, Vish, Muhammad Danso
    10 Friday, 10th November Attention, MT, LAS Slides (.pdf)
    Video (YT)
    Rucha Manoj Kulkarni & Muhammed Danso
    Hackathon Saturday, 11th November HW4 Harini Subramanyan, Liangze "Josh" Li, Miya Sylvester, Muhammad Danso, Schadrack Niyibizi
    11 Friday, 17th November Transformers Transformer Notebook
    ViT Notebook
    Video (YT)
    Vish & Yohannes Haile
    Hackathon Saturday, 18th November HW4 Harshit Mehrotra, Schadrack Niyibizi
    12 Friday, 1st December Graph Neural Networks Colab Notebook
    Video (YT)
    Denis Musinguzi & Kelsey J. Harvey
    Hackathon Saturday, 2nd December HW4 TBA TBA Schadrack Niyibizi
    13 Friday, 8th December Autoencoders and VAEs (GANs) TBA TBA Harshith Arun Kumar & Emmanuel Ndayisaba


    ∑ Ongoing, ∏ Upcoming

    Assignment Release Date (EST) Due Date (EST) Related Materials / Links
    HW1P1 Wednesday, 6th Sept, 12:00 AM Early Submission: 14th Sept, 11:59 PM Autolab
    Final: 23rd Sept, 11:59 PM
    HW1P2 Wednesday, 6th Sept, 12:00 AM Early Submission: 14th Sept, 11:59 PM Kaggle, Writeup (pdf),
    Starter Notebook
    Final: 24th Sept, 11:59 PM
    HW1 Bonus Wednesday, 6th Sept, 12:00 AM Saturday, 14th Oct, 11:59 PM Autolab
    HW1 Autograd Wednesday, 6th Sept, 12:00 AM Saturday, 21st Oct, 11:59 PM Autolab
    HW2P1 Monday, 25th Sept, 12:00 AM Early Submission: 5th Oct, 11:59 PM Autolab
    Final: 22nd Oct, 11:59 PM
    HW2P2 Monday, 25th Sept, 12:00 AM Early Submission: 5th Oct, 11:59 PM Kaggle-Classification
    Writeup (pdf)
    Starter Notebook
    Final: 22nd Oct, 11:59 PM
    HW2 Bonus Monday, 25th Sept, 12:00 AM Saturday, 4th Nov, 12:00 AM Autolab
    HW2 Autograd Monday, 25th Sept, 12:00 AM Saturday, 11th Nov, 12:00 AM Autolab
    HW3P1 Monday, 23rd Oct, 12:00 AM Early Submission: 31st Oct, 11:59 PM Autolab, Writeup (pdf),
    Handout (.tar)
    Final: 9th Nov, 11:59 PM
    HW3P2 Monday, 23rd Oct, 12:00 AM Early Submission: 31st Oct, 11:59 PM Kaggle, Writeup (pdf),
    Starter Notebook, MCQ
    Final: 9th Nov, 11:59 PM
    HW3 Bonus TBA TBA
    HW3 Autograd TBA TBA
    HW4P1 Monday, 6th Nov, 12:00 AM Early Submission: 16th Nov, 11:59 PM Autolab, Writeup (pdf),
    Handout (.tar)
    Final: 2nd Dec, 11:59 PM
    HW4P2 Monday, 6th Nov, 12:00 AM Early Submission: 16th Nov, 11:59 PM Kaggle, Writeup (pdf),
    Starter Notebook
    Final: 2nd Dec, 11:59 PM
    HW4 Bonus TBA TBA
    HW4 Autograd TBA TBA

    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