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F22 S22
11-785 Introduction to Deep Learning
Fall 2022
Class Streaming Link

In-Person Venue: Giant Eagle Auditorium, Baker Hall

Bulletin and Active Deadlines

Assignment Deadline Description Links
HW3P2 (slack) Released: 19th Nov, 11:59 PM EDT
Utterance to Phoneme Mapping Kaggle (slack),
Writeup (*.pdf)
HW4P1 Early Bonus: 26th Nov, 11:59 PM EDT
Final: 9th Dec, 11:59 PM EDT
Language Modeling using RNNs Writeup(*.pdf),
Handout(*.zip)
HW4P2 Early: 26th Nov, 11:59 PM EDT
Final: 9th Dec, 11:59 PM EDT
Attention-based End-to-End Speech-to-Text Deep Neural Network Kaggle,
Writeup(*pdf)
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 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.

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

  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.

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:

TAs:

Pittsburgh Schedule (Eastern Time)

Lecture: Mondays and Wednesdays, from 8:35 AM to 9:55 AM EDT

Recitation: Fridays, from 8:35 AM to 9:55 AM

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: We will be using OHQueue for zoom related Office hours, others would be in-person. The OH schedule is given below.

Day Time (Eastern Time) TA Zoom/In Person Venue
Monday 12:00PM to 1:00PM Karanveer Singh Zoom
2:30PM to 4:30PM Fuyu Tang TCS 349
4:30PM - 6:30PM Oscar Joris Denys Zoom / Room 224 (SV)
6:00PM to 7:00PM Swathi Jadav GHC 5417
Tuesday 9:00 AM to 11:00 AM Soumya Empran Zoom
12:30PM to 2:30PM Yue Jian Wean 3110
3:00 PM to 4:00 PM Ruoyu Hua Zoom
4:00PM to 6:00PM Samruddhi Pai Zoom
6:30PM to 7:30PM Samiran Gode Wean 3110
Wednesday 11:00AM to 12:00PM Zishen Wen GHC 5417
12:00PM to 2:00PM Yashash Gaurav Wean 3110
2:00PM to 3:00PM Talha Faiz Zoom
3:00PM to 4:00PM Moayad Elamin Zoom
6:00 PM to 7:00 PM Swathi Jadav Wean 3110
8:00 PM to 9:00 PM Spatika Ganesh Zoom
Thursday 10:00AM to 11:00AM Cedric Manouan Zoom
12:00PM to 1:00PM Aditya Singh Wean 3110
2:00PM to 3:00PM Talha Faiz Zoom
3:00PM to 5:00PM Abuzar Khan Wean 3110
5:00PM to 7:00PM Ameya Mahabaleshwarkar Zoom
7:00 PM to 9:00 PM Pranav Karnani GHC 5417
8:00 PM to 9:00 PM Spatika Ganesh Zoom
Friday 10:00AM to 11:00AM Shreyas Piplani Zoom
11:00AM to 12:00PM Moayad Elamin Zoom
12:00PM to 1:00PM Cedric Manouan Zoom
1:00PM to 2:00PM Vishhvak Srinivasan GHC 5417
3:00 PM to 5:00 PM George Saito Wean 3110
5:00PM to 7:00PM Aparajith Srinivasan Zoom / Wean 3110
8:00 PM to 9:00 PM Ruoyu Hua Zoom
Saturday 9:00AM to 12:00PM (CDT) Homework Hackathon (Kigali) Auditorium A203
10:00AM to 11:00AM Shreyas Piplani Zoom
2:00PM to 5:00PM Homework Hackathon (Pittsburgh) Wean RM7500
5:00PM to 6:00PM Aditya Singh Zoom
6:00PM to 7:00PM Samiran Gode Wean 3110
Sunday 12:00PM to 1:00PM Karanveer Singh Zoom
3:00PM to 4:00PM Vishhvak 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: Wean Hall, Rm. 7500, Saturday afternoons from 2 PM to 5 PM EDT, beginning 3rd Sept and ending on 3rd Dec. (except 29th Oct)

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 7 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.
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: 10% - Midterm Report; 35% - Project Video; 5% - Responding to comments on Piazza; 50% - 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 may either watch the real-time zoom lectures or the recorded lectures on
    • 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; see the forms on the bulletin.

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 TBA 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 Monday,
29 Aug
  • Introduction
Slides (*.pdf)
Video (YT)
The New Connectionism (1988)
On Alan Turing's Anticipation of Connectionism
Quiz 1
2 Wednesday,
31 Aug
  • Neural Nets As Universal Approximators
Slides (*.pdf)
Video (YT)
Shannon (1949)
Boolean Circuits
On the Bias-Variance Tradeoff
3 Friday,
2 Sep
  • 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 Wednesday,
7 Sep
  • Empirical risk minimization and gradient descent
  • Training the network: Setting up the problem
Slides (*.pdf)
Video (YT)
Werbos (1990)
Rumelhart, Hinton and Williams (1986)
5 Monday,
12 Sep
  • Backpropagation
  • Calculus of Backpropagation
Slides (*.pdf)
Video (1/2) (YT)
Video (2/2) (YT)
Werbos (1990)
Rumelhart, Hinton and Williams (1986)
Quiz 3
6 Wednesday,
14 Sep
  • 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,
19 Sep
  • 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,
21 Sep
  • 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)
9 Monday,
26 Sep
  • Shift invariance and Convolutional Neural Networks
Slides (*.pdf)
Video (YT)
Quiz 5
10 Wednesday,
28 Sep
  • Models of vision, Convolutional Neural Networks
Slides (*.pdf)
Video (YT)
11 Monday,
3 Oct
  • Learning in Convolutional Neural Networks
Slides (*.pdf)
Video (YT)
CNN Explainer Quiz 6
12 Wednesday,
5 Oct
  • Learning in CNNs
  • Transpose Convolution
  • CNN Stories
Slides (*.pdf)
Video (YT)
13 Monday,
10 Oct
  • 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 Wednesday,
12 Oct
  • Stability and Memory, LSTMs
Slides (*.pdf)
Video (YT)
Bidirectional Recurrent Neural Networks
- Monday,
17 Oct
  • No Class - Fall Break
- Quiz 8
- Wednesday,
19 Oct
  • No Class - Fall Break
-
15 Monday,
24 Oct
  • Sequence Prediction
  • Alignments and Decoding
Slides (*.pdf)
Video (YT)
LSTM Quiz 9
16 Wednesday,
26 Oct
  • Sequence prediction
  • Connectionist Temporal Classification (CTC) - Blanks and Beam-search
Slides (*.pdf)
Video (YT)
17 Monday,
31 Oct
  • Language Models
  • Sequence To Sequence Prediction
Slides (*.pdf)
Video (YT)
Labelling Unsegmented Sequence Data with Recurrent Neural Networks Quiz 10
18 Wednesday,
2 Nov
  • 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!
19 Monday,
7 Nov
  • Transformers and GNNs
Slides (*.pdf)
Video (YT)
A comprehensive Survey on Graph Neural Networks Quiz 11
20 Wednesday,
9 Nov
  • Learning Representations
  • AutoEncoders
Slides (*.pdf)
Video (YT)
21 Monday,
14 Nov
  • Variational Auto Encoders
Slides (*.pdf)
Video (YT)
Redo Lecture Video (YT)
Tutorial on VAEs (Doersch)
Autoencoding variational Bayes (Kingma)
Quiz 12
22 Wednesday,
16 Nov
  • Variational Auto Encoders II
Slides (*.pdf)
Video (YT)
23 Friday,
18 Nov
  • Generative Adversarial Networks, 1
Slides (*.pdf)
Video (YT)
24 Monday,
21 Nov
  • Generative Adversarial Networks, 2
Slides (*.pdf)
Video (YT)
Quiz 13
- Wednesday,
23 Nov
  • No Class - Thanksgiving
-
25 Monday,
28 Nov
  • Hopfield Nets and Auto Associators
Slides (*.pdf)
Video (YT)
Quiz 14
26 Wednesday,
30 Nov
  • Hopfield Nets and Boltzmann Machines
Slides (*.pdf)
Video (YT)
27 Monday,
5 Dec
  • Hopfield Nets and Boltzmann Machines 2
Slides (*.pdf)
Video (YT)
No Quiz
28 Wednesday,
7 Dec
  • Deep Learning in the Real World - Agot.AI (Guest Lecture)
Video (YT)

Schedule of Recitations

Recitation Date Topics Materials Videos Instructor
0A Monday, 15th Aug Python & OOP Fundamentals Notebook (*.zip)

Video (YT): 1, 2

Zishen Wen, Fuyu Tang
0B Monday, 15th Aug Fundamentals of NumPy Notebook (*.zip)

Video (YT): 1, 2, 3, 4, 5, 6, 7

Pranav Karnani, Karanveer Singh
0C Monday, 15th Aug PyTorch Tensor Fundamentals Notebook (*.zip)

Video (YT): 1, 2

Ruoyu Hua, Soumya Empran
0D Monday, 15th Aug Dataset & DataLoaders Notebook + Slides (*.zip) Video (YT)
Samiran Gode
0E Monday, 15th Aug Introduction to Google Colab Notebook (*.zip) Video (YT)
Aditya Singh
0F Monday, 15th Aug Debugging, Monitoring Notebook (*.zip) Video (YT): 1, 2
Oscar Joris Denys
0G Monday, 15th Aug AWS Fundamentals Notebook + Slides (*.zip) Video (YT): 1, 2, 3, 4

Yashash Gaurav, George Saito
0H Monday, 15th Aug WandB Notebook (*.zip) Video (YT)
Moayad Elamin
0I Monday, 15th Aug What to do if you're struggling Slides (*.pdf) Video (YT)
Vishhvak Srinivasan, Yue Jian
0J Monday, 15th Aug Data Preprocessing Slides (*.pdf)

Video (YT): 1, 2, 3

Samruddhi Pai, Abuzar Khan
1 Friday, 2nd Sep Your first MLP Code Slides (*.pdf) Video (YT)
Fuyu Tang, Zishen Wen
HW1 Bootcamp Tuesday, 6th Sep How to get started with HW1 Video (YT)
Karanveer Singh, Ruoyu Hua
2 Friday, 9th Sep Network Optimization & Hyperparameter Tuning Slides (*.pdf) Video (YT)
Vishhvak Srinivasan, Swathi Jadav
3 Friday, 16th Sep Computing Derivatives & Autograd Slides (*.pdf) Video (YT)
Zishen Wen, Talha Faiz, and Fuyu Tang
4 Friday, 23rd Sep Hyperparameter Tuning Methods, Normalizations, Ensemble Methods, Study Groups Slides (*.pdf) Video (YT)
Samruddhi Pai, Moayad Elamin
5 Friday, 30th Sep CNN: Basics & Backprop Slides (*.pdf)
Colab Notebook
Video (YT)
Abuzar Khan, Ruoyu Hua
HW2 Bootcamp Thursday, 6th Oct How to get started with HW2 Resources (*.zip)
Video (YT)
Yashash Gaurav, Pranav Karnani
6 Friday, 7th Oct CNNs: Classification & Verification Slides(*.pdf) Video (YT)
Cedric Manouan, Aditya Singh
7 Friday, 21st Oct Paper Writing Workshop Slides (*.pdf) Video (YT)
Karanveer Singh, Moayad Elamin, Shreyas Piplani
8 Friday, 24th Oct RNN Basics (Pre-recorded) Slides (*.pdf)
Code (*.ipynb)
Video (YT)
Samiran Gode, Shreyas Piplani, Soumya Empran
9 Friday, 28th Oct CTC, Beam Search CTC Slides (*.pdf) Beam-search Slides (*.pdf) Video (YT)
Soumya Empran, Ameya Mahabaleshwarkar
HW3 Bootcamp Tuesday, 1st Nov How to get started with HW3 HW3P1 Slides (*.pdf)
HW3P2 Slides (*.pdf)
Bootcamp Notebook (colab)
Video (YT)
Pranav Karnani, Abuzar Khan, Aparajith Srinivasan
10 Friday, 4th Nov Attention, MT, LAS Recitation Slides (*.zip) Intro to PSC Video (YT)
Aparajith Srinivasan, Vishhvak Srinivasan
11 Friday, 11th Nov Transformers Part 1 - Video (YT)
Part 2 - Video (YT)
Samiran Gode, Yue Jian
HW5 Bootcamp Tuesday, 15th Nov GANs and How to get started with HW5 Code (*.ipynb) Video (YT)
Aparajith Srinivasan
HW4 Bootcamp Friday, 18th Nov How to get started with HW4 HW4P1 Explainer Slides (*.pdf)
HW4P2 Theory Slides (*.pdf)
HW4P2 Starter Code Slides (*.pdf)
HW4P1 Explainer - Video (YT)
HW4P2 Theory - Video (YT)
HW4P2 Starter Code - Video (YT)
Moayad Elamin, Swathi Jadav, George Saito
12 Friday, 2nd Dec Graph Neural Networks Slides (*.ipynb)
Handout (*.rar)
Video (YT)
Yue Jian, George Saito
13 Friday, 9th Dec YOLO Yashash Gaurav, Samruddhi Pai, Talha Faiz

Assignments

∑ Ongoing, ∏ Upcoming

Assignment Release Date Due Date Related Materials / Links
HW0P1 Saturday, 20th Aug Final: 8th Sept, 11:59 PM Autolab, Handout
(see recitation 0s)
HW0P2 Saturday, 20th Aug Final: 8th Sept, 11:59 PM AutolabHandout
(see recitation 0s)
HW1P1 Sunday, 4th Sept Early Bonus: 15th Sept, 11:59 PM Autolab, Writeup (pdf), Handout (.tar)
Final: 29th Sept, 11:59 PM
HW1P2 Sunday, 4th Sept Early: 15th Sept, 11:59 PM Kaggle, Writeup (pdf)
Final: 29th Sept, 11:59 PM
HW1 Bonus Friday, 30th Sept Final: 26th Oct, 11:59 PM Autolab, Writeup (pdf), Handout (.tar)
Project Proposal Monday, 10th Oct, 12 AM EST Wednesday, 14th Oct, 11:59 PM EST
HW2P1 Friday, 30th Sept Early Bonus: 14th Oct, 11:59 PM Autolab,
Writeup,
Handout (.tar)
Final (ext.): 29th Oct, 11:59 PM
HW2P2 Friday, 30th Sept Early: 14th Oct, 11:59 PM Face Classification: Kaggle,
Face Verification: Kaggle,
Writeup (*.pdf)
Final: 27th Oct, 11:59 PM
Project Midterm Report - Friday, 11th Nov
HW3P1 Friday, 28th Oct Early Bonus: 3rd Nov, 11:59 PM Autolab,
Handout,
Writeup
Final: 17th Nov, 11:59 PM
HW3P2 Friday, 28th Oct Early: 3rd Nov, 11:59 PM Kaggle,
Canvas Quiz,
Writeup (*.pdf)
Final: 19th Nov, 11:59 PM
HW4P1 Friday, 18th Nov Early Bonus: 26th Nov, 11:59 PM Writeup(*.pdf),
Handout(*.zip)
Final: 9th Dec, 11:59 PM
HW4P2 Friday, 18th Nov Early: 26th Nov, 11:59 PM Kaggle,
Writeup(*pdf)
Final: 9th Dec, 11:59 PM
Final Project Video Presentation & Preiliminary Project Report - 9th Dec, 11:59 PM
Project Peer reviews 10th Dec, 12:00 AM
11th Dec, 11:59 AM -
Final Project Report Submission - 14th Dec, 11:59 PM

Documentation and Tools

Textbooks

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