# 11-785 Deep Learning

https://deeplearning.cs.cmu.edu/S25

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**11-785** Introduction to Deep Learning 
_Spring 2025_   
[ Class Streaming Link ](https://cmu.zoom.us/j/98857399276?pwd=ems3MzMvMjNQYkZzdTAyWGdVRGZSUT09)
In-Person Venue: Giant Eagle Auditorium, Baker Hall (A51)
  

##  Active Deadlines and Bulletin   
| Assignment  | Deadline  | Description  | Links  |  
| --- | --- | --- | --- |  
 |  
|  **[ Most Important Piazza Post ](https://piazza.com/class/m4cvt38ufqk155/post/15) **  |  
|  **[ Project Gallery ](https://deeplearning.cs.cmu.edu/shared/project.html) **  |  
### 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](https://www.youtube.com/channel/UC8hYZGEkI2dDO8scT8C5UQA). 
### Course Description from a Student's Perspective
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 HW5 respectively.   
Course 11-485 is the undergraduate version worth 9 units, the only difference being that there is no final project or HW5. 
### Your Supporters
**Instructors:**
  * **Bhiksha Raj** : bhiksha@cs.cmu.edu
  * **Rita Singh** : rsingh@cs.cmu.edu


**TAs:**
  * **Kateryna Shapovalenko** : kshapova@andrew.cmu.edu
  * **Miya Sylvester** : nsylvest@andrew.cmu.edu
  * **Alexander Moker** : amoker@andrew.cmu.edu
  * **Purusottam Samal** : psamal@andrew.cmu.edu
  * **Shravanth Srinivas** : shravans@andrew.cmu.edu
  * **Yuzhou Wang** : yuzhouwa@andrew.cmu.edu
  * **Massa Baali** : mbaali@andrew.cmu.edu
  * **Vedant Singh** : vhsingh@andrew.cmu.edu
  * **Sadrishya Agrawal** : sadrisha@andrew.cmu.edu
  * **Michael Kireeff** : mkireeff@andrew.cmu.edu 
  * **Vishan Oberoi** : voberoi@andrew.cmu.edu
  * **Ishita Gupta** : ishitag@andrew.cmu.edu
  * **Shubham Kachroo** : skachroo@andrew.cmu.edu
  * **Shrey Jain** : shreyj@andrew.cmu.edu
  * **Floris Nzabakira** : fnzabaki@andrew.cmu.edu
  * **Christine Muthee** : cmuthee@andrew.cmu.edu
  * **Ahmed Issah** : aissah@andrew.cmu.edu
  * **Shubham Singh** : shubham4@andrew.cmu.edu
  * **Tanghang Elvis Tata** : etanghan@andrew.cmu.edu
  * **John Liu** : johnliu@andrew.cmu.edu
  * **Damilare Olatunji** : dolatunj@andrew.cmu.edu
  * **Brian Ebiyau** : bebiyau@andrew.cmu.edu
  * **Peter Wauyo** : pwauyo@andrew.cmu.edu
  * **Eman Ansar** : eansar@andrew.cmu.edu


### Acknowledgments
**Wall of fame**
[ Past TA Acknowledgments](https://deeplearning.cs.cmu.edu/shared/TAs.html)
### Pittsburgh Schedule (Eastern Time)
**Lecture:** Monday and Wednesday, 8:00 a.m. - 9:20 a.m. - Good times :) 
**Recitation Labs:** Friday, 8:00 a.m. - 9:20 a.m. 
**Office Hours:** Please refer the below **OH Calendar** / **Piazza** for up-to-date information. 
**Homework Hackathon:** During 'Homework Hackathons', students will be assisted with homework by the course staff. It is recommended to come as study groups.  
**Every Saturday**
  * Location: **TBD**
  * Time: **Saturday 2-5 PM EST**


**Event Calendar:** The Google Calendar below contains all course 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 reflected in this 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 will be reflected in this calendar first. 
  

## Syllabus  
| **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, plus the Peer Review assignment during the last week of the semester. Assignments will include _Autolab_ components, where you implement low-level operations, and a _Kaggle_ component, where you compete with your colleagues over relevant DL tasks. 
  * 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. 
    * **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** , with each of the four HWs being 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. More details will be shared over Piazza.

 |  
| **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 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): [Link](https://docs.google.com/spreadsheets/d/1nguV-Z45QJkQUambJ4dv-SijA_ttsDQnwUYlbBOzicw/edit#gid=0). 

 |  
| **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](https://mediaservices.cmu.edu/channel/11785+-+Introduction+to+Deep+Learning+\(Spring+2025\))). 
  * If viewed on MediaServices, the lectures of each week must be viewed before Monday 8AM of the following week (otherwise, it doesn’t count). 


* At the end of the semester, we will select a random subset of 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
We believe that effective collaboration can greatly enhance student learning. Thus, this course employs study groups for both quizzes and homework ablations. 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 [here](https://www.piazza.com). Also, please follow the Piazza Etiquette when you use the piazza forum.
### 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](https://kaggle.com/) 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 Recitation Recordings
CMU students who are not in the live lectures should watch the uploaded lectures at **[MediaServices](https://mediaservices.cmu.edu/channel/11785-+Introduction+to+Deep+Learning/)** in order to get attendance credit. Links to individual videos will be posted as they are uploaded.
[Our YouTube Channel](https://www.youtube.com/channel/UC8hYZGEkI2dDO8scT8C5UQA) 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](http://www.datasciencecentral.com/profiles/blogs/concise-visual-summary-of-deep-learning-architectures). 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](https://www.cmu.edu/policies/student-and-student-life/academic-integrity.html). 
  * 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](http://mlsp.cs.cmu.edu/people/rsingh/IDLbook.html). 
## Schedule of Lectures
You can watch the recorded lectures on [MediaServices](https://mediaservices.cmu.edu/channel/11785-+Introduction+to+Deep+Learning/).   
| Lecture  | Date  | Topics  | Slides, Videos  | Additional Materials  | Quiz  |  
| --- | --- | --- | --- | --- | --- |  
| 0  | Friday,  
Jan 03  | 
  * Course Logistics
  * Learning Objectives
  * Grading
  * Deadlines

 |  [Youtube](https://www.youtube.com/watch?v=ClawCx_UKb0)  
 |   |  No Quiz   |  
| 1  | Monday,  
Jan 13  | 
  * Introduction

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec1.intro.pdf)  
[MediaServices](https://mediaservices.cmu.edu/channel/11785+-+Introduction+to+Deep+Learning+\(Spring+2025\)/)  
[Youtube](https://youtu.be/NXYrIEP1LRs)  
 |  [The New Connectionism (1988)](https://deeplearning.cs.cmu.edu/S25/document/readings/Perceptrons-Epilogue-r.pdf)   
[On Alan Turing's Anticipation of Connectionism](http://www.alanturing.net/turing_archive/pages/pub/turing3/turing3.pdf)  
[ McCullogh and Pitts paper](https://www.cs.cmu.edu/~./epxing/Class/10715/reading/McCulloch.and.Pitts.pdf)  
[Rosenblatt: The perceptron](https://deeplearning.cs.cmu.edu/S25/document/readings/Rosenblatt_1959-09865-001.pdf)  
[Bain: Mind and body](https://deeplearning.cs.cmu.edu/S25/document/readings/Alexander_Bain_Mind_and_Body_009178a0.pdf)  
[Hebb: The Organization Of Behaviour](https://pure.mpg.de/pubman/item/item_2346268_3/component/file_2346267/Hebb_1949_The_Organization_of_Behavior.pdf)  
 |  Quiz 1   |  
| 2  | Wednesday,  
Jan 15  | 
  * Neural Nets As Universal Approximators

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec2.universal.pdf)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Neural+Networks_What+can+a+network+represent/1_ty0pand6/365819512)  
[Youtube](https://youtu.be/bAOKjnqH1Vg)  
 |  [Shannon (1949)](https://deeplearning.cs.cmu.edu/S25/document/readings/Shannon49.pdf)  
[Boolean Circuits](https://deeplearning.cs.cmu.edu/S25/document/readings/booleancircuits_shannonproof.pdf)  
[On the Bias-Variance Tradeoff](https://www.bradyneal.com/bias-variance-tradeoff-textbooks-update)  |  
| 3  | Friday,  
Jan 17  | 
  * Training Part I
    * The Problem of Learning
    * Empirical Risk Minimization

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec3.learning.pdf)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Neural+Networks_Learning+the+Network_Part+1/1_26fhbch3/365819512)  
[Youtube](https://youtu.be/t3HVjD84mSg)  
 |  [Widrow and Lehr (1992)](https://deeplearning.cs.cmu.edu/S25/document/readings/c1992artificialneural.pdf)  
[Adaline and Madaline](https://deeplearning.cs.cmu.edu/S25/document/readings/04Adaline.pdf)  
 |  Quiz 2   |  
| -  | Monday,  
Jan 20  | 
  * No class (MLK day)

 |   |   |  
| 4  | Wednesday,  
Jan 22  | 
  * Training Part II
    * Gradient Descent
    * Training the Network
    * Backpropagation

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec4.learning.pdf) [MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Neural+Networks_Neural+Networks_Learning+the+network_Backprop/1_jkwt0mb3/365819512)  
[Youtube](https://youtu.be/i6FSITIMgWg?si=IJVGF0w0mh3INDJf)  
 |  [Widrow and Lehr (1992)](https://deeplearning.cs.cmu.edu/S25/document/readings/c1992artificialneural.pdf)  
[Adaline and Madaline](https://deeplearning.cs.cmu.edu/S25/document/readings/04Adaline.pdf)  
[Convergence of perceptron algorithm](http://www.cs.columbia.edu/~mcollins/courses/6998-2012/notes/perc.converge.pdf)  
[Threshold Logic](https://www.tutorialspoint.com/digital_circuits/digital_circuits_threshold_logic.htm)  
[TC (Complexity)](https://en.wikipedia.org/wiki/TC_\(complexity\))  
[AC (Complexity)](https://en.wikipedia.org/wiki/AC_\(complexity\))  |  
| 5  | Monday,  
Jan 27  | 
  * Training Part III
    * Backpropagation
    * Calculus of Backpropagation

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec5.pdf)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Neural+Networks_Neural+Network_Learning+the+network_Part+3/1_5zje3zcb/365819512)  
[Youtube](https://youtu.be/t4nxlSD5hyE?si=gUtEwcmhfJFNJJnl)  
 |  [Werbos (1990)](http://axon.cs.byu.edu/Dan/678/papers/Recurrent/Werbos.pdf)  
[Rumelhart, Hinton and Williams (1986)](http://www.cs.toronto.edu/~hinton/absps/naturebp.pdf)  |  Quiz 3   |  
| 6  | Wednesday,  
Jan 29  | 
  * Training Part IV
    * Convergence issues
    * Loss Surfaces
    * Momentum

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec6.pdf)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Neural+Networks_Optimization+Part+1/1_xqfpcyfa/365819512)  
[Youtube](https://youtu.be/ew6HGXuCQ9s?si=TVzNj-A5u92_mXMZ)  |  [Backprop fails to separate, where perceptrons succeed, Brady et al. (1989)](https://ieeexplore.ieee.org/document/31314)  
[Why Momentum Really Works](https://distill.pub/2017/momentum/)  |  
| 7  | Monday,  
Feb 3  | 
  * Training Part V
    * Optimization
    * Batch Size, SGD, Mini-batch, Second-order Methods

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec7.stochastic_gradient.pdf)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Training+Neural+Networks_Optimization/1_nofidwmj)  
[Youtube](https://youtu.be/U2eKCK5wZII)  
 |  [ Momentum, Polyak (1964)](https://www.sciencedirect.com/science/article/abs/pii/0041555364901375)  
[ Nestorov (1983)](http://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=dan&paperid=46009&option_lang=eng)  
[Derivatives and Influences](https://deeplearning.cs.cmu.edu/S25/document/readings/derivatives_and_influences.pdf)  
 |  Quiz 4   |  
| 8  | Wednesday,  
Feb 5  | 
  * Training Part VI
    * Optimizers and Regularizers
    * Choosing a Divergence (Loss) Function
    * Batch Normalization
    * Dropout

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec8.optimizersandregularizers.pdf)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Training+Neural+Networks_Normalization%2C+Regularization%2C+etc-/1_b942bona)  
[Youtube](https://youtu.be/qGAsXzdVsAQ)  
 |  [Derivatives and Influence Diagrams](https://deeplearning.cs.cmu.edu/S25/document/readings/derivatives%20and%20influences.pdf)  
[ ADAGRAD, Duchi, Hazan and Singer (2011)](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)  
[Adam: A method for stochastic optimization, Kingma and Ba (2014)](https://arxiv.org/abs/1412.6980)  
 |  
| 9  | Monday,  
Feb 10  | 
  * Shift Invariance
  * Convolutional Neural Networks (CNNs) - Part I

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec9.CNN1.pdf)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Deep+Neural+Networks_Scanning+for+patterns_aka+convolution+networks/1_zxg7tskc)  
[Youtube](https://youtu.be/w5bIH9gDsZA)  
 |   |  Quiz 5   |  
| 10  | Wednesday,  
Feb 12  | 
  * Convolutional Neural Networks (CNNs) - Part II
  * Models of Vision and CNNs

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec10.CNN2.pdf)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Deep+Neural+Networks_Scanning+for+patterns_aka+convolution+networks/1_lc9nxeee/365819512)  
[Youtube](https://youtu.be/HDDyfCKotuk)  
 |   |  
| 11  | Monday,  
Feb 17  | 
  * Convolutional Neural Networks (CNNs) - Part III
  * Learning in CNNs

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec11.CNN3.pdf)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Convolutional+Networks+III/1_rfsrv4dz)  
[Youtube](https://www.youtube.com/watch?v=kYeeB3CNcx8)  
 |  [CNN Explainer](https://poloclub.github.io/cnn-explainer/)  |  Quiz 6   |  
| 12  | Wednesday,  
Feb 19  | 
  * Convolutional Neural Networks (CNNs) - Part IV
  * Learning in CNNs
  * Transpose Convolution
  * CNN Stories

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec12.CNN4.pdf)  
[Youtube](https://www.youtube.com/watch?v=t2MB4fIdBmg)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Convolutional+Networks+IV/1_eqmcs47s/365819512)  
 |   |  
| 13  | Monday,  
Feb 24  | 
  * Time Series and Recurrent Networks

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec13.recurrent.pdf)  
[Youtube](https://youtu.be/lbvxLWkHeoc)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Recurrent+Networks+1/1_jw7ll9x2/365819512)  
 |  [ Fahlman and Lebiere (1990)](https://proceedings.neurips.cc/paper/1989/file/69adc1e107f7f7d035d7baf04342e1ca-Paper.pdf)  
[ How to compute a derivative, extra help for HW3P1 (*.pptx)](https://deeplearning.cs.cmu.edu/S25/document/readings/How%20to%20compute%20a%20derivative.pdf)  
 |  Quiz 7   |  
| 14  | Wednesday,  
Feb 26  | 
  * Stability and Memory, LSTMs

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec14.recurrent.pdf)  
[Youtube](https://youtu.be/djst-4ZEuhs)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Recurrent+Networks_Stability+Analysis+and+LSTMs/1_ufid7y9z/365819512)  
 |   |  
| -  | Monday,  
Mar 3  | 
  * No class (Spring break)

 |   |   |  Makeup 1-7 Quiz   |  
| -  | Wednesday,  
Mar 5  | 
  * No class (Spring break)

 |   |   |  
| 15  | Monday,  
Mar 10  | 
  * Sequence Prediction
  * Alignments and Decoding

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec15.recurrent.pdf)  
[Youtube](https://youtu.be/Pt9-IgIfOls)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Recurrent+Networks_Part+3/1_x9bonxd2)  
 |   |  Quiz 8   |  
| 16  | Wednesday,  
Mar 12  | 
  * Sequence Prediction
  * Connectionist Temporal Classification
    * Blanks
    * Beam Search

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec16.recurrent.pdf)  
[Youtube](https://youtu.be/_f1F0jgEW14)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Sequence+to+Sequence+Models/1_vk6svzc6)  
 |   |  
| 17  | Monday,  
Mar 17  | 
  * Language Models
  * Sequence To Sequence Predictions

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec17.recurrent.pdf)  
[Youtube](https://youtu.be/PEOV8xaPenA)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Sequence+to+Sequence+Models/1_vk6svzc6)  
 |  [ Labeling Unsegmented Sequence Data with Recurrent Neural Networks](https://www.cs.toronto.edu/~graves/icml_2006.pdf)  |  Quiz 9   |  
| 18  | Wednesday,  
Mar 19  | 
  * Attention models
  * Transformers

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec18.attention.pdf)  
[Youtube](https://youtu.be/wCKpRTqykic)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Sequence+to+Sequence+Models/1_vk6svzc6)  
 |  [Attention Is All You Need](https://arxiv.org/pdf/1706.03762.pdf)  
[ The Annotated Transformer - Attention is All You Need paper, but annotated and coded in PyTorch!](http://nlp.seas.harvard.edu/annotated-transformer/)  |  
| 19  | Monday,  
Mar 24  | 
  * Transformers and Newer Architectures

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec19.transformer.pdf)  
[Youtube](https://youtu.be/5PowTTh207w)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Transformer+and+New+Architectures/1_t5ps3y1c)  
 |   |  Quiz 10   |  
| 20  | Wednesday,  
Mar 26  | 
  * Large Language Models

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec20.LLMs.pdf)  
[Youtube](https://youtu.be/Nr98BmCSUPc) [MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Guest+Lecture/1_gnecqciw)  
 |   |  
| 21  | Monday,  
Mar 31  | 
  * Representation and Autoencoders

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec21.representations.pdf)  
[Youtube](https://youtu.be/SYp1vkl0CSI)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Neural+Networks+Representations/1_6tglz7hs)  
 |   |  Quiz 11   
Part 1   |  
| 22   | Wednesday,  
Apr 02  | 
  * Variational Auto Encoders

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec22.VAE.pdf)  
[Youtube](https://youtu.be/F5Rub6-aquw)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Variational+Auto+Encoders/1_3jyvew1h)  
 |   |  Quiz 11   
Part 2   |  
| 23  | Monday,  
Apr 07  | 
  * Diffusion

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lec23.diffusion_s25.pdf)  
[Youtube](https://youtu.be/YiZPC-QBchE)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Diffusion/1_pmaxhj5i)  
 |   |  Quiz 12   |  
| 24  | Wednesday,  
Apr 09  | 
  * Generative Adversarial Networks I and II

 |  [Slides Part 1 (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/GANs_Lec24_Part1.pdf)  
[Slides Part 2 (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/GAN_Part2.pdf)  
[Youtube](https://youtu.be/UWrQjTE2kG4)  
[MediaServices](https://mediaservices.cmu.edu/media/Introduction+to+Deep+Learning_Spring+2025_Generative+Adversarial+Networks/1_as7wuzqz)  
 |  [Jensen-Shannon divergence (JSD)](https://deeplearning.cs.cmu.edu/S25/document/slides/jsd.pdf)  
 |  
| 25  | Monday,  
Apr 14  | 
  * Hopfield Networks

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lecture_25_Hopfield_S25.pdf)  
[Youtube](https://www.youtube.com/watch?v=SFgluM04rak)  
 |   |  Quiz 13   |  
| 26  | Wednesday,  
Apr 16  | 
  * Boltzmann Machines

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/lecture_26_BM_S25.pdf)  
 |   |  
| 27  | Monday,  
Apr 21  | 
  * Guest lecture on GNNs (John Shi and Shreyas Chaudhari)

 |  [Slides (PDF)](https://deeplearning.cs.cmu.edu/S25/document/slides/deeplearninggraphcnns.pptx.pdf)  
[Youtube](https://www.youtube.com/watch?v=by50wVhdl8k)  
 |   |   |  
| 28  | Wednesday,  
Apr 23  | 
  * Guest lecture / buffer

 |   |   |   |  
##  Recitations/Labs and Bootcamps  
| Recitation  | Date  | Group  | Topics  | Materials  | Youtube Videos  | Instructor  |  
| --- | --- | --- | --- | --- | --- | --- |  
| 0.1  | Monday,  
Jan 02  | Python Programming + Pytorch  | Python + OOP Fundamentals  |  [ Python Fundamentals](https://colab.research.google.com/drive/1rayerPGak04_EUjKgr5j0QAGh5UF9nLO)  
[ OOP Fundamentals](https://colab.research.google.com/drive/1c4CwtF3zX-INs5Q0F7ni7s_PBsVS_Nfc)  |  [Python-1](https://youtu.be/QNnaTJbBBfo), [Python-2](https://youtu.be/gUqFI01F5-A), [OOP-1](https://youtu.be/ZZDfeXUZcLI), [OOP-2](https://youtu.be/mnAHmHDk1dk)  | Eman Ansar,  
Yichen Xin,   
Carmel Prosper SAGBO  |  
| 0.2  | Numpy Fundamentals + JAX  |  [ Numpy Notebook](https://colab.research.google.com/drive/13YtF8PWq7381T2x3ZOryocmGqnO3HCU8)  
[ Broadcasting Pitfalls & JAX Notebook](https://colab.research.google.com/drive/1_unLDgM-S8BAmG3jmjQSCgO0VeYBPijW)  |  [Numpy-1](https://youtu.be/FHkt09pPekc),  
[Numpy-2](https://youtu.be/wORlQwFOsaA),  
[Numpy-3](https://youtu.be/tO9XEMDyLI0),  
[Numpy-4](https://youtu.be/fZq0V81xFMY),  
[Broadcasting Pitfalls & Jax](https://youtu.be/dKxezBq0zGI)  | Shubham Singh,  
Michael Kireeff  |  
| 0.3  | Notebooks and Conda Environments   |   |  [Link](https://youtu.be/1zYsdq6Inks)  | Vishan Tanghang,  
Elvis Tata  |  
| 0.4  | Pytorch  |  [ Pytorch Notebook](https://colab.research.google.com/drive/1ZWGtC75ZZoDTeTH7NqCSmii2pE8-3qUi)  |  [Link](https://youtu.be/jIv_g9Cz4Xg)  | Shravanth Srinivas,  
Vishan Oberoi  |  
| 0.5  | Computational Resources   | Google Cloud Platform  |  [ VM Setup Script](https://colab.research.google.com/drive/1RFMHtpog3JAjQ-4O6GSXUztKRVhZwZIw)  |  [GCP-1](https://youtu.be/rURvnI3jC7k), [GCP-2 (optional)](https://youtu.be/NNLVWO0bYJA)  | Alexander Moker,  
John Liu  |  
| 0.6  | Google Colab  |  [ Notebook](https://colab.research.google.com/drive/1IpFJjPSNFcPSF9WUbpC9BUnsPsPszqhL)  |  [Link](https://youtu.be/8Hhg8zt4qb4)  | Shubham Kachroo,  
Sadrishya Agrawal  |  
| 0.7  | AWS  |  [ AWS Notebook](https://colab.research.google.com/drive/1-Ta2zqTuXzsnZI3PjvehQrjHY9dmRKAW)  |  [Link](https://youtu.be/4oJVHPvf_T4)  | Tanghang Elvis Tata,  
Peter Wauyo  |  
| 0.8  | Kaggle  |  [ AWS Notebook](https://colab.research.google.com/drive/1VvKmX6i5oNsEzAGXC7mYJx1XP5VXaFnT)  |  [Link](https://youtu.be/vw-ptlTDk0Y)  | Olatunji Damilare E.,  
Floris Nzabakira  |  
| 0.9  | PSC  |   |  [Link](https://youtu.be/stOd0AtI9Qw)  | Alexander Moker,  
Shrey Jain  |  
| 0.10  | Data Handling and Processing  | Datasets & Dataloaders  |  [ Datasets P1 Notebook](https://colab.research.google.com/drive/1W1WbYX3xXTaECQeptk3FZo3nAwATwsGr),   
[ Datasets P2 Notebook](https://colab.research.google.com/drive/1NnjOfGZZytRd1_6pnUOH72H3MGsnvV8Q),   
[ Dataloader Notebook](https://colab.research.google.com/drive/1Y0kbDPFR-6Hxlz4kooQbpkVQfdTP6EKq)  |  [Datasets-1](https://youtu.be/s67KlJDxOWQ),  
[Datasets-2](https://youtu.be/7jeun5QJv1I),  
[Dataloader-1](https://youtu.be/kXYQIr2BetU),  
[Dataloader-2](https://youtu.be/Ve86WJVqM1o),  
 | Vedant Singh,  
Christine Muthee  |  
| 0.11  | Data Preprocessing  |  [ Audio Preprocessing Notebook](https://colab.research.google.com/drive/1T2ZDWlQSli37QiUmU-oxFB-ChawNO5bP),  
[ Image Preprocessing Notebook](https://colab.research.google.com/drive/13F6Rz9alqpALGWsAjzemPonBs9S9O5ED)  |  [Data Preprocessing-1 (Audio)](https://youtu.be/s_gs_uhAMr0),  
[Data Preprocessing-2 (Image)](https://youtu.be/ZYyYGyBtiDU)  | Vedant Singh,  
Eman Ansar  |  
| 0.12  | Debugging and Problem Solving  | Debugging  |  [ Debugging Notebook (Part I)](https://colab.research.google.com/drive/1uIHxQunIjkcBJvqQmX5DNJKnV_D9tisg)  
[ Debugging Notebook (Part III)](https://colab.research.google.com/drive/1rSOy2gs2Os04txABLX0k78pukfTap0y2)  |  [Debugging-1](https://youtu.be/uCp44RCzf0I), [Debugging-3](https://youtu.be/i53KwCvz8ls)  | Khushali Daga,  
Romerik Lokossou  |  
| 0.13  | What to Do When Struggling  |   |  [Part 1](https://youtu.be/N2LXNeCKDCA), [Part 2](https://youtu.be/x8-rYtcXB8U)  | Christine Muthee,  
Shravanth Srinivas  |  
| 0.14  | HWs and Project Workflow Management  | Workflow of HWs  |   |  [Link](https://youtu.be/vtBxC_FsgD8)  | Alexander Moker,  
Syed Abdul Hannan  |  
| 0.15  | Wandb  |  [ WandB Notebook](https://colab.research.google.com/drive/1un_9ceP1AJ12-OLDOECrc9NQERS7v2nY)  |  [Link](https://youtu.be/Vi9LBmKteiU?si=SDBGWx-zAexw3i7i)  | Floris Nzabakira,  
Ahmed Issah  |  
| 0.16  | Git  |   |  [Link](https://youtu.be/kLCW0OOa2UU)  | Sadrishya Agrawal,  
Shubham Kachroo  |  
| 0.17  | Flow of the Project & How to Write a Report  |   |  [Link](https://youtu.be/wPBkq-BzUYM)  | Olatunji Damilare E.,  
Yuzhou Wang  |  
| 0.18  | Algorithmic Techniques   | Losses  |  [ Losses P1 Notebook](https://colab.research.google.com/drive/1URxJnfeJtiQF8YVem7jik46saLA3nInD),  
[ Losses P2 Notebook](https://colab.research.google.com/drive/1aICyWy-F6LyYoxVkG1i69-1f8e_XrztI)  |  [Losses-1](https://youtu.be/_kVxbL_nyBk),  
[Losses-2](https://youtu.be/ih3v4lZSozk)  | Massa Baali,  
Shrey Jain  |  
| 0.19  | Block Processing  |  [ Block Processing Notebook](https://colab.research.google.com/drive/1LHoxuxHWDUwTIyP573xyUD2ze_7pLAMR)  |  [Link](https://youtu.be/bJzLegB6Aj4)  | Puru Samal,  
Eman Ansar  |  
| 0.20  | Model Logistics  | Paper to Code  |  [ Notebook](https://colab.research.google.com/drive/1dFLjpGVV5Xe4JHF1mpelgnGMrtExSPQz)  |  [Link](https://youtu.be/czcn0mOsx58)  | Massa Baali,  
Peter Wauyo  |  
| 0.21  | Pipeline  |   |  [Link](https://youtu.be/NIhlx5QVEWc)  | Puru Samal  |  
| 0.22  | Distributed Training  |   |  [Link](https://youtu.be/rkgBnPT9dek)  | Ishita Gupta,  
Harshith Arun Kumar  |  
| 0.23  | Saving & Loading Model (Checkpointing)  |  [ Saving & Loading Notebook](https://colab.research.google.com/drive/1-4ORZZtIAd1FFbd_bv_l5quZ2yJROtIa)  |  [Link](https://youtu.be/6EtsRk1PQIU)  | Ahmed Issah,  
John Liu  |  
| 0.24  | Cheating  |   |  [Link](https://youtu.be/WEUeQfv6lpY?si=VIEfvap7jycxOb0R)  | Andy Ye,  
Eman Ansar,   
Dheeraj Mohandas Pai  |  
| Lab 1  | Saturday,  
Jan. 18th   |   | Your First MLP  |  [ Colab Notebook](https://colab.research.google.com/drive/1Erm7kOLMSQ6gkzzEXQ6TMXjRJ0zjPyQe#scrollTo=d8a41c18)  |  [Lab 1](https://www.youtube.com/watch?v=iX6bEiCOEUc)  
[HW1 Bootcamp](https://youtu.be/skrUmhPl1hQ?si=G3-FiJ73OnqYofC7)  
[Derivatives Bootcamp](https://youtu.be/BFFgU3FBJoU?si=RFYOKAV91ARolHvg)  |  Miya Sylvester,  
Vedant Singh  |  
| HW1 Bootcamp  |   | HW1P1, HW1P2  |  [ Slides](https://docs.google.com/presentation/d/10iDr60RWUn8-BznIihnOSSNLHjRDd_zHY7RlkT4TW4A/edit?usp=sharing)  | Shubham Singh,   
Shubham Kachroo,  
Shravanth Srinivas,  
Issah Ahmed,  
Vishan Oberoi   |  
| Lab 2  | Friday,  
Jan. 24th  |   | Debugging in Deep Learning Networks   |  [ Colab Notebook](https://piazza.com/redirect/s3?bucket=uploads&prefix=paste%2Fl79gsjtlsas6t4%2Fe30c0f407ce5388832d436246b81141036d0243b9bd33c618af1bbe1755ca580%2FLab_2_-_Notebook_%28STUDENT_VERSION%29.ipynb)   
[ Data ](https://piazza.com/redirect/s3?bucket=uploads&prefix=paste%2Fl79gsjtlsas6t4%2Fcc5c7dd1c06cd0e777e92cbd9b91834a0cde2296eb1b7ae5dee0725237096b5c%2Fdata.zip)  |  [Link](https://youtu.be/IbqcgVRluUo)  |  Kateryna Shapovalenko,  
Vishan Oberoi  |  
| Lab 3  | Friday,  
Jan. 31st  |   | Network Optimizations  |  [ Notebook](https://piazza.com/redirect/s3?bucket=uploads&prefix=paste%2Fllsfmaqyiv937n%2F33ad3f35d1bff59d15bd9ac6c58f92fef9f43cfc3bd74e6776b93376ba67c242%2FSTUDENT_IDL_S25_Lab_3_Network_Optimizations.ipynb)  |  [Link](https://youtu.be/sIbwFRL8Reo)  |  Shubham Kachroo,  
Tanghang Elvis Tata  |  
| Lab 4  | Friday,  
Feb. 7th  |   | Computing Derivatives and Autograd   |  [ Slides](https://cdn-uploads.piazza.com/paste/ljjjuqvroyk192/8c947613aea2b348abf1492c38824004ef7cec5b27f4f2f13eadd530783e1394/Computing_Derivatives___Autograd_Lab_4.pdf)  
[ Notebook P1 ](https://piazza.com/redirect/s3?bucket=uploads&prefix=paste%2Fl751bkyneri128%2F0eda61a4f2b55d09f5ad64ccd48ad61e3e4871955f55dc25c02d73d1441a7707%2FIDL_S25_Lab_4_Computing_Derivatives___Autograd.ipynb)  
[ Notebook P2 ](https://piazza.com/redirect/s3?bucket=uploads&prefix=paste%2Fl751bkyneri128%2Fe240179f1a294f84a23e77870873c7c3cf08440e8a6f06a6fad848d106751434%2FIDL-S24-Lab_4_P2_-_Intro_to_IDL_Autograd_Engine.ipynb)  
[ Solutions P1 ](https://colab.research.google.com/drive/1DfuQHPp2ZjMjLT6BkANtSk3Lpq4aIdeU?usp=sharing)  |  [Link](https://youtu.be/pnjkqSQMHmA)  |  Michael Kireeff,  
John Liu  |  
| HW2 Bootcamp  | Saturday,  
Feb. 8th   |   | HW2P1, HW2P2  |  [ HW2P1 Slides](https://deeplearning.cs.cmu.edu/S25/document/slides/HW2P1_Bootcamp.pdf)  
[ HW2P2 Slides](https://deeplearning.cs.cmu.edu/S25/document/slides/S25-HW2P2-BOOTCAMP.pdf)  |  [Link](https://youtu.be/95P-FT7GXCw)  |  Sadrishya Agrawal,  
Michael Kireeff,   
John Liu,   
Tanghang Elvis Tata,   
Christine Muthee  |  
| Lab 5  | Friday,  
Feb. 14th  |   | CNN: Basics  |   |   |  Sadrishya Agrawal,  
Shravanth Srinivas  |  
| Lab 6  | Friday,  
Feb. 21st  |   | CNN Classification and Verification   |   |   |  Shravanth Srinivas,  
Tanghang Elvis Tata  |  
| Lab 7  | Friday,  
Feb. 28st  |   | RNN Basics  |   |   |  Alexander Moker,  
Michael Kireeff  |  
| Lab 8 (Pre recorded)  | Friday,  
Mar. 8th  |   | Kaggle Competitions  |  [ Slides ](https://docs.google.com/presentation/d/1NVZCo8frgq1MUt7LtzGU_9ScrlwD8DG_/edit?usp=sharing&ouid=110837114422260679754&rtpof=true&sd=true)  |  [Link](https://drive.google.com/file/d/1L9oP1XeB3fE86O1QXSSsWLcosNj7pwmH/view?usp=sharing)  
 |  Christine Muthee,  
Shubham Singh  |  
| HW3 Bootcamp  | Saturday,  
Mar. 9th   |   | HW3P1, HW3P2  |  [ HW3P1 Slides](https://deeplearning.cs.cmu.edu/S25/document/slides/HW3P1%20Bootcamp.pdf)  
[ HW3P2 Slides](https://deeplearning.cs.cmu.edu/S25/document/slides/HW3P2%20Bootcamp%20Presentation.pdf)  |  [Link](https://www.youtube.com/watch?v=oDe6tfaKNrc)  |  Ishita Gupta,  
Tanghang Elvis Tata,  
Shubham Singh,   
Christine Muthee,   
Floris Nzabakira,   
Ahmed Issah  |  
| Lab 9  | Friday,  
Mar. 14th  |   | CTC, Beam Search  |   |   |  Purusottam Samal,  
Ishita Gupta  |  
| HW5 Bootcamp  | Saturday,  
Mar. 15th   |   |   |   |  [ Link](https://youtu.be/9_Z2H1MT6QE)  
 |  Massa Baali  |  
| Lab 10  | Friday,  
Mar. 21st  |   | Attention, MT, LAS  |   |   |  Alexander Moker,  
Peter Wauyo  |  
| Lab 11  | Friday,  
Mar. 28th  |   | Transformers  |   |  [Link](https://youtu.be/9bFg6aah3r8)  |  Floris Nzabakira,  
Ahmed Issah  |  
| HW4 Bootcamp  | Saturday,  
Mar. 29th   |   | HW4P1, HW4P2  |   |  [Link](https://youtu.be/bRgLPBMldrU)  |  Purusottam Samal,  
Yuzhou Wang,   
Vedant Singh,   
Michael Kireeff,   
Ishita Gupta  |  
| PSC HW4 Guide  | Sunday,  
April 13th   |   | PSC  |   |  [Link](https://youtu.be/mKblwdm7wBU)  |  John Liu   |  
| Lab 12 (Pre recorded)  | Friday,  
Apr. 4th  |   | VAE  |   |   |  Purusottam Samal,  
Massa Baali  |  
| Lab 13  | Friday,  
Apr. 11th  |   | NF and Stable Diffusion  |   |   |  Yuzhou Wang,  
Massa Baali,   
Shrey Jain   |  
| Lab 14  | Friday,  
Apr. 18th  |   | GAN  |   |   |  Ishita Gupta,  
Peter Wauyo  |  
| Lab 15  | Friday,  
Apr. 25th  |   | Graph Neural Networks  |   |   |  Vedant Singh,  
Floris Nzabakira  |  
##  Assignments   
| Assignment  | Release Date (EST)  | Due Date (EST)  | Related Materials / Links  |  
| --- | --- | --- | --- |  
| HW1P1  | Friday, Jan 17 11:59 PM  |  Early Deadline: Friday, Jan 24 11:59 PM  
On-Time Deadline: Friday, Feb 7 11:59 PM   |  [Autolab](https://autolab.andrew.cmu.edu/courses/11485-s25/assessments/HW1P1)  |  
| HW1P2  |  [Autolab](https://autolab.andrew.cmu.edu/courses/11485-s25/assessments/hw1p2)  |  
| HW1P1 Bonus  | Friday, Apr 25 11:59 PM  |  [Autolab](https://autolab.andrew.cmu.edu/courses/11485-s25/assessments/hw1p1_bonus)  |  
| HW1P1 Autograd  | Friday, Apr 25 11:59 PM  |  [Autolab](https://autolab.andrew.cmu.edu/courses/11485-s25/assessments/hw1p1autograd)  |  
| HW2P1  | Friday, Feb 7 11:59 PM   |  Early Deadline: Friday, Feb 21 11:59 PM  
On-Time Deadline: Saturday, Mar 1 11:59 PM   |  [Autolab](https://autolab.andrew.cmu.edu/courses/11485-s25/assessments/HW2P1_)   
[Piazza](https://piazza.com/class/m4cvt38ufqk155/post/383)  |  
| HW2P2  |  [Piazza](https://piazza.com/class/m4cvt38ufqk155/post/350)  |  
| HW2P1 Bonus  | Friday, Apr 25 11:59 PM  |  [Autolab](https://autolab.andrew.cmu.edu/courses/11485-s25/assessments/HW2P1)  |  
| HW2P1 Autograd  | Friday, Apr 25 11:59 PM  |  [Autolab](https://autolab.andrew.cmu.edu/courses/11485-s25/assessments/autogradcnn)  |  
| HW3P1  | Saturday, Mar 1 11:59 PM  |  Early Deadline: Friday, Mar 14 11:59 PM  
On-Time Deadline: Friday, Mar 28 11:59 PM   |  [Piazza](https://piazza.com/class/m4cvt38ufqk155/post/718)  
 |  
| HW3P2  |  [Piazza](https://piazza.com/class/m4cvt38ufqk155/post/706)  
 |  
| HW4P1  | Friday, Mar 28 11:59 PM  |  Early Deadline: Friday, Apr 11 11:59 PM  
On-Time Deadline: Friday, Apr 25 11:59 PM   |  [Piazza](https://piazza.com/class/m4cvt38ufqk155/post/1036)  
 |  
| HW4P2  |  [Piazza](https://piazza.com/class/m4cvt38ufqk155/post/1035)  
 |  
## Documentation and Tools
### Textbooks
This is a selection of optional textbooks you may find useful
![Deep Learning](https://deeplearning.cs.cmu.edu/S25/image/dive.png)
[**Dive Into Deep Learning**](http://d2l.ai) By Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola _PDF, 2020_
![Deep Learning](https://deeplearning.cs.cmu.edu/S25/image/Goodfellow.jpg)
[**Deep Learning**](https://www.deeplearningbook.org/) By Ian Goodfellow, Yoshua Bengio, Aaron Courville _Online book, 2017_
![Neural Networks and Deep Learning](https://deeplearning.cs.cmu.edu/S25/image/Nielsen.jpg)
[**Neural Networks and Deep Learning**](http://neuralnetworksanddeeplearning.com/) By Michael Nielsen _Online book, 2016_
![Deep Learning Step by Step with Python](https://deeplearning.cs.cmu.edu/S25/image/Lewis.jpg)
[**Deep Learning Step by Step with Python: A Very Gentle Introduction to Deep Neural Networks for Practical Data Science**](https://www.amazon.com/Deep-Learning-Step-Python-Introduction/dp/1535410264) By N. D. Lewis
![Parallel Distributed Processing](https://deeplearning.cs.cmu.edu/S25/image/Rumelhart1.jpg)
[**Parallel Distributed Processing, Volume 1**](https://mitpress.mit.edu/books/parallel-distributed-processing-volume-1) By Rumelhart and McClelland
![Parallel Distributed Processing](https://deeplearning.cs.cmu.edu/S25/image/Rumelhart2.jpg)
[**Parallel Distributed Processing, Volume 2**](https://mitpress.mit.edu/books/parallel-distributed-processing-volume-2) By Rumelhart and McClelland