11-785 Deep Learning

11-785 Introduction to Deep Learning

Spring 2023
Class Streaming Link

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

Bulletin and Active Deadlines

Assignment	Deadline	Description	Links
HW3P1	Early Submission: 25th March, 11:59 PM Final Submission: 7th April, 11:59 PM	RNNs, GRUs and Search	Autolab
HW3P2	Early Submission: 25th March, 11:59 PM Final Submission: 7th April, 11:59 PM	Utterance to Phoneme Mapping	Kaggle Writeup
HW4P1	Early Submission: 15th Apr, 11:59 PM Final Submission: 28th Apr, 11:59 PM	Language Modeling	Autolab
HW4P2	Early Submission (Mandatory) - Canvas Quiz : 10th Apr, 11:59 PM Early Submission Bonus (Optional) - Kaggle : 15th Apr, 11:59 PM Final Submission: 28th Apr, 11:59 PM	Automatic Speech Recognition using Attention-based Seq2Seq Models	Kaggle Writeup
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.

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.

Prerequisites

We will be using Numpy and PyTorch in this class, so you will need to be able to program in python3.
You will need familiarity with basic calculus (differentiation, chain rule), linear algebra and basic probability.

Units

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.

Acknowledgments

Your Supporters

Instructors:

Bhiksha Raj : bhiksha@cs.cmu.edu
Rita Singh : rsingh@cs.cmu.edu

TAs:

Abuzar Khan: abuzark@andrew.cmu.edu
Aparajith Srinivasan: aparajis@andrew.cmu.edu
Arjun Chauhan: arjunc@andrew.cmu.edu
Cedric Manouan: cmanoun@andrew.cmu.edu
Eshani Agrawal: eshania@andrew.cmu.edu
Harshith Kumar: harunkum@andrew.cmu.edu
Joseph Konan: jkonans@andrew.cmu.edu
Liangze "Josh" Li: liangzel@andrew.cmu.edu
Miya Sylvester: nsylvest@andrew.cmu.edu
Prakruthi Pradeep: prakruth@andrew.cmu.edu
Qin Wang: qinw@andrew.cmu.edu
Sarthak Bisht: sbisht@andrew.cmu.edu
Shikhar Agnihotri: sagnihot@andrew.cmu.edu
Swathi Jadav: sjadav@andrew.cmu.edu
Varun Jain: vjain2@andrew.cmu.edu
Vedant Bhasin: vedantb@andrew.cmu.edu
Vishhvak Srinivasan: vishhvas@andrew.cmu.edu
Yooni Choi: jungyoon@andrew.cmu.edu
(SV) Ruimeng Chang: ruimengc@andrew.cmu.edu
(Kigali) Yonas Chanie: ychanie@andrew.cmu.edu
(Kigali) Paul Ewuzie: pewuzie@andrew.cmu.edu

Pittsburgh Schedule (Eastern Time)

Lecture: Tuesday and Thursday, 12:30 p.m. - 1:50 p.m. - Good times :')

Recitation: Friday, 12:30 p.m. - 1:50 p.m.

Event Calendar: The Google Calendar below ideally contains all events and deadlines for student's convenience. 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	9:00AM to 10:00AM	Prakruthi Pradeep	Zoom
	11:00AM to 12:00PM	Arjun Chauhan	In-person
	2:00PM to 3:00PM	Eshani Agrawal	In-person
	8:00PM to 9:00PM	Swathi Jadav	Zoom
Tuesday	8:00AM to 9:00AM	Yooni Choi	Zoom
	9:30AM to 10:30AM	Liangze "Josh" Li	Zoom
	10:30AM to 11:30AM	Yonas Charlie	Zoom
	3:00PM to 4:00PM	Qin Wang	In-person
	4:00PM to 6:00PM	Vish	In-person
	6:00PM to 7:00PM	Harshith Arun Kumar	In-person
Wednesday	8:00AM to 9:00AM	Yooni Choi	Zoom
	9:00AM to 10:00AM	Prakruthi Pradeep	Zoom
	11:00AM to 12:00PM	Arjun Chauhan	In-person
	12:00PM to 1:00PM	Abu	In-person
	2:00PM to 3:00PM	Eshani Agrawal	In-person
	6:30 PM to 7:30 PM	Swathi Jadav	In-person
Thursday	9:30AM to 10:30AM	Liangze "Josh" Li	Zoom
	11:00AM to 12:00PM	Abu	In-person
	2:00PM to 3:00PM	Vedant Bhasin	In-person
	3:00PM to 5:00PM	Varun Jain	Zoom
	6:00PM to 7:00PM	Harshith Arun Kumar	In-person
Friday	10:30AM to 11:30AM	Yonas Charlie	Zoom
	11:30AM to 12:30PM	Paul Ewuzie	Zoom
	2:00PM to 3:00PM	Vedant Bhasin	In-person
	5:00PM to 7:00PM	Sarthak Bisht	In-person
Saturday	9:00AM to 10:00AM	Paul Ewuzie	Zoom
	10:00AM to 11:00AM	Shikhar Agnihotri	Zoom
	6:00PM to 7:00PM	Ruimeng Chang	Zoom
Sunday	9:00AM to 11:00AM	Shikhar Agnihotri	In-person
	12:00PM to 1:00PM	Ruimeng Chang	Zoom
	1:00PM to 2:00PM	Qin Wang	In-person
	6:00PM to 8:00PM	Aparajith Srinivasan	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: BH A36/A136
Time: Saturday 2-5 PM EST

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 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), .0.9max(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: 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.
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 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
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; 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)		Quiz 0A Quiz 0B
1	Tuesday, 17 Jan	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	Thursday, 19 Jan	Neural Nets As Universal Approximators	Slides (*.pdf) Video (YT) (F22 in-person lecture link)	Shannon (1949) Boolean Circuits On the Bias-Variance Tradeoff	Quiz 1
3	Tuesday, 24 Jan	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)	Quiz 2
4	Thursday, 26 Jan	Empirical risk minimization and gradient descent Training the network: Setting up the problem	Slides (*.pdf) Video (YT)	Werbos (1990) Rumelhart, Hinton and Williams (1986)	Quiz 2
5	Tuesday, 31 Jan	Backpropagation Calculus of Backpropagation	Slides (*.pdf) Video (YT)	Werbos (1990) Rumelhart, Hinton and Williams (1986)	Quiz 3
5.5	Wednesday, 1 Feb	Backpropagation (Extra Lecture) Calculus of Backpropagation	Video (YT) Extra Lecture
6	Thursday, 2 Feb	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	Tuesday, 7 Feb	Optimization Batch Size, SGD, Mini-batch, second-order methods	Slides (*.pdf) Video (YT)	Momentum, Polyak (1964) Nestorov (1983) Derivatives and Influences	Quiz 4
8	Thursday, 9 Feb	Optimizers and Regularizers Choosing a divergence (loss) function Batch normalization Dropout	Slides (*.pdf) Video (YT)	Derivatives and Influence Diagrams ADAGRAD, Duchi, Hazan and Singer (2011) Adam: A method for stochastic optimization, Kingma and Ba (2014)	Quiz 4
9	Tuesday, 14 Feb	Shift invariance and Convolutional Neural Networks	Slides (*.pdf) Video (YT)		Quiz 5
10	Thursday, 16 Feb	Models of vision, Convolutional Neural Networks	Slides (*.pdf) Video (YT)		Quiz 5
11	Tuesday, 21 Feb	Learning in Convolutional Neural Networks	Slides (*.pdf) Video (YT)	CNN Explainer	Quiz 6
12	Thursday, 23 Feb	Learning in CNNs Transpose Convolution CNN Stories	Slides (*.pdf) Video (YT)		Quiz 6
13	Tuesday, 28 Feb	Time Series and Recurrent Networks	Slides (*.pdf) Video (YT)	Fahlman and Lebiere (1990) How to compute a derivative, extra help for HW3P1 (*.pptx)	Quiz 7
14	Thursday, 2 Mar	Stability and Memory, LSTMs	Slides (*.pdf) Video (YT)	Bidirectional Recurrent Neural Networks	Quiz 7
-	Tuesday, 7 Mar	No Class - Spring Break	-		Quiz 8
-	Thursday, 9 Mar	No Class - Spring Break	-		Quiz 8
15	Tuesday, 14 Mar	Sequence Prediction Alignments and Decoding	Slides (*.pdf) Video (YT)	LSTM	Quiz 9
16	Thursday, 16 Mar	Sequence prediction Connectionist Temporal Classification (CTC) - Blanks and Beam-search	Slides (*.pdf) Video (YT)		Quiz 9
17	Tuesday, 21 Mar	Language Models Sequence To Sequence Prediction	Slides (.pdf) Slides (.pdf) Video (YT)	Labelling Unsegmented Sequence Data with Recurrent Neural Networks	Quiz 10
18	Thursday, 23 Mar	Sequence To Sequence Methods Attention	Slides (*.pdf) Video (YT)	Attention Is All You Need The Annotated Transformer - Attention is All You Need paper, but annotated and coded in pytorch!	Quiz 10
19	Tuesday, 28 Mar	Transformers and GNNs	Slides (*.pdf) Video (YT)	A comprehensive Survey on Graph Neural Networks	Quiz 11
20	Thursday, 30 Mar	Learning Representations AutoEncoders	Slides (*.pdf) Video (YT)		Quiz 11
21	Tuesday, 4 Apr	Variational Auto Encoders	Slides (*.pdf) Video (YT)	Tutorial on VAEs (Doersch) Autoencoding variational Bayes (Kingma)	Quiz 12
22	Thursday, 6 Apr	Variational Auto Encoders II	Slides (*.pdf) Video (YT)		Quiz 12
23	Tuesday, 11 Apr	Generative Adversarial Networks, 1	Slides (*.pdf)		Quiz 13
-	Thursday, 13 Apr	No Class - Spring Carnival	-	-	Quiz 13
24	Tuesday, 18 Apr	Generative Adversarial Networks, 2	TBA		Quiz 14
25	Thursday, 20 Apr	Hopfield Nets and Auto Associators	TBA		Quiz 14
26	Tuesday, 25 Apr	Hopfield Nets and Boltzmann Machines	TBA		No Quiz
27	Thursday, 27 Apr	Boltzmann Machines	TBA		No Quiz

Schedule of Recitations

Recitation	Date	Topics	Materials	Videos	Instructor
0A	Friday, 13th Jan	Python & OOP Fundamentals	Notebook (*.zip)	Video (YT): 1, 2	Paul Ewuzie
0B	Friday, 13th Jan	Fundamentals of NumPy	Notebook (*.zip)	Video (YT): 1, 2, 3, 4, 5, 6, 7, 8	Prakruthi Pradeep
0C	Friday, 13th Jan	PyTorch Tensor Fundamentals	Notebook + Slides + Cheatsheet (*.zip)	Video (YT): 1	Sarthak Bisht
0D	Friday, 13th Jan	Dataset & DataLoaders	Notebook + Slides (*.zip)	Video (YT)	Eshani Agrawal
0E	Friday, 13th Jan	Introduction to Google Colab	Notebook (*.zip)	Video(YT)	Yooni Choi
0F	Friday, 13th Jan	AWS Fundamentals	Slides (*.zip)	Video (YT): 1, 2, 3, 4	Ruimeng Chang, Arjun
0G	Friday, 13th Jan	Debugging, Monitoring	Notebook + Slides (*.zip)	Video (YT): 1, 2, 3	Qin Wang
0H	Friday, 13th Jan	Basics of Git	Colab Notebook	Video (YT)	Varun Jain
0I	Friday, 13th Jan	Wandb		Video (YT)
0J	Friday, 13th Jan	What to do if you're struggling	Slides (*.zip)	Video (YT)	Aparajith Srinivasan
0K	Friday, 13th Jan	Data Preprocessing	Notebook + Slides (*.zip)	Video (YT)	Yonas Chanie
0L	Friday, 13th Jan	Google Cloud		Video (YT)	Vish
0M	Friday, 13th Jan	Workflow of a Deeplearning Homework	Notebook + Slides (*.zip)	Video (YT)	Vedant Bhasin
1	Friday, 20th Jan	Your first MLP Code	Slides (*.pdf) , Notebook 1a, Notebook 1b, Notebook 1c	Video (YT)	Sarthak Bisht, Yooni Choi
2	Friday, 27th Jan	Network Optimization, Hyperparameter Tuning	Slides (*.pdf)	Video (YT)	Sarthak Bisht, Varun Jain
HW1 Bootcamp	Saturday, 28th Jan	How to get started with HW1	Slides (*.zip)	Video (YT)	Eshani, Sarthak, Yooni
3	Friday, 3rd Feb	Computing Derivatives & Autograd	Slides (.pdf) Notebook (.ipynb)	Video (YT)	Sarthak Bisht, Ruimeng Chang
HW1 Hackathon	Saturday, 4th Feb	Tips with HW1	Slides (*.zip)	Video (YT)	Swathi, Sarthak
4	Friday, 10th Feb	Paper Writing Workshop	Slides (*.pdf)	Video (YT)	Joseph, Miya
HW1 Hackathon	Saturday, 11th Feb	Tips with HW1P2	Slides (*.zip)	Video (YT)	Sarthak
5	Friday, 17th Feb	CNN: Basics & Backprop	Slides (*.pdf) Colab Notebook	Video (YT)	Yooni, Eshani
6	Friday, 24th Feb	CNNs: Classification & Verification	Slides(*.pdf)	Video (YT)	Prakruthi, Paul
HW2 Bootcamp	Saturday, 25th Feb	How to get started with HW2	Resources (*.zip)	Video (YT)	Qin Wang, Vedant Bhasin, Ruimeng Chang
7	Friday, 3rd Mar	CNNs: Verification, Code	Slides (*.pdf)	Video (YT)	Prakruthi Pradeep, Arjun Chauhan
8	Friday, 10th Mar	RNN Basics (Pre-recorded)	Slides (.pdf) Code (.ipynb)	Video (YT)	Paul Ewuzie
9	Friday, 17th Mar	CTC, Beam Search	CTC Slides (.pdf) Beam-search Slides (.pdf)	Video (YT)	Eshani Agrawal, Shikhar Agnihotri
HW3 Bootcamp	Saturday, 18th Mar	How to get started with HW3	HW3P1 Slides (.pdf) HW3P2 Slides (.pdf)	Video (YT)	Eshani Agrawal, Harshith Kumar, Vedant Bhasin
10	Friday, 24th Mar	Attention, MT, LAS	Slides	Video (YT)	Swathi Jadav, Liangze "Josh" Li
11	Friday, 31st Mar	Transformers	Slides Colab Notebook	Video (YT)	Vish, Vedant
12	Friday, 7th Apr	Graph Neural Networks	Slides (*.ipynb)	Video (YT)	Vish, Josh
HW4 Bootcamp	Wednesday, 12th Apr	How to get started with HW4		HW4P1 - Video (YT) HW4P2 - Video (YT)	Varun, Vish, Prakruthi, Josh
13	Friday, 21st Apr	Autoencoders and VAEs
14	Friday, 28th Apr	Deep Reinforcement Learning		Video (YT) Video (YT)
HW5 Bootcamp		GANs and How to get started with HW5

Assignments

∑ Ongoing, ∏ Upcoming

Assignment	Release Date (EST)	Due Date (EST)	Related Materials / Links
HW0P1	Friday, 13th Jan, 11:59 PM	Final: 30th Jan, 11:59 PM	Autolab, Handout (see recitation 0s)
HW0P2	Friday, 13th Jan, 11:59 PM	Final: 30th Jan, 11:59 PM	Autolab, Handout (see recitation 0s)
HW1P1	Sunday, 22nd Jan, 11:59 PM	Early Submission: 11th Feb, 11:59 PM	Autolab, Writeup (pdf), Handout (.tar)
HW1P1	Sunday, 22nd Jan, 11:59 PM	Final: 17th Feb, 11:59 PM	Autolab, Writeup (pdf), Handout (.tar)
HW1P2	Sunday, 22nd Jan, 11:59 PM	Early Submission: 11th Feb, 11:59 PM	Kaggle, Writeup (pdf), Starter Notebook, MCQ
HW1P2	Sunday, 22nd Jan, 11:59 PM	Final: 17th Feb, 11:59 PM	Kaggle, Writeup (pdf), Starter Notebook, MCQ
HW1 Bonus	Sunday, 22nd Jan, 12:00 AM	Friday, 10th Mar, 11:59 PM	Autolab
HW1 Autograd	Sunday, 22nd Jan, 12:00 AM	Wednesday, 3rd May, 11:59 PM	Autolab, Writeup (pdf), Handout (.tar)
Project Proposal	Monday, 27th Feb, 11:59 PM
HW5	Monday, 20th Feb, 11:59 PM	Thursday, 27th Apr, 11:59 PM EST
HW2P1	Friday, 17th Feb, 11:59 PM	Early Submission: 4th Mar, 11:59 PM	Writeup, Autolab
HW2P1	Friday, 17th Feb, 11:59 PM	Final: 17th Mar, 11:59 PM	Writeup, Autolab
HW2P2	Friday, 17th Feb, 11:59 PM	Early Submission: 4th Mar, 11:59 PM	Face Classification: Kaggle, Face Verification: Kaggle, Writeup (*.pdf), Starter Notebook
HW2P2	Friday, 17th Feb, 11:59 PM	Final: 17th Mar, 11:59 PM
HW2 Bonus	Friday, 17th Feb, 11:59 PM	Friday, 31st March, 11:59 PM	Autolab,
HW2 Autograd	Friday, 5th March, 11:59 PM	Wednesday, 28th April, 11:59 PM	Autolab
Project Midterm Report	-	Friday, 31st March, 11:59 PM
HW3P1	Friday, 10th Mar, 11:59 PM	Early Submission: 26th Mar, 11:59 PM	Autolab, Writeup
HW3P1	Friday, 10th Mar, 11:59 PM	Final: 7th Apr, 11:59 PM	Autolab, Writeup
HW3P2	Friday, 17th Mar, 11:59 PM	Early Submission: 26th Mar, 11:59 PM	Kaggle, Canvas Quiz, Writeup (*.pdf)
HW3P2	Friday, 17th Mar, 11:59 PM	Final: 7th Apr, 11:59 PM	Kaggle, Canvas Quiz, Writeup (*.pdf)
HW2 Autograd	Friday, 5th Mar, 11:59 PM	Wednesday, 28th April, 11:59 PM	Autolab
HW4P1	Friday, 31st Mar, 11:59 PM	Early Submission Bonus: 15th Apr, 11:59 PM	Autolab, Writeup (.pdf), Handout (.zip)
HW4P1	Friday, 31st Mar, 11:59 PM	Final: 28th Apr, 11:59 PM	Autolab, Writeup (.pdf), Handout (.zip)
HW4P2	Friday, 4th Apr, 11:59 PM	Early Submission Canvas Quiz (Mandatory): 10th Apr, 11:59PM Kaggle Cutoff Bonus (Optional): 15th Apr, 11:59 PM	Kaggle, Writeup(*pdf)
HW4P2	Friday, 4th Apr, 11:59 PM	Final: 28th Apr, 11:59 PM	Kaggle, Writeup(*pdf)
HW5	Friday, 1st Apr, 11:59 PM	Early: 18th Apr, 11:59 PM	Kaggle, Writeup(*pdf), Handout
HW5	Friday, 1st Apr, 11:59 PM	Final: 1st May, 11:59 PM	Kaggle, Writeup(*pdf), Handout
Final Project Video Presentation & Preiliminary Project Report	-	27th Apr, 11:59 PM
Project Peer reviews			-
Final Project Report Submission	-	3rd May, 11:59 PM