A shoutout to TA Farrukh Rahman who was active and remained extremely polite throughout the semester. The dominant method for achieving this, artificial neural networks, has revolutionized the processing of data (e.g. Some are good, but I feel like Dr. Kira could do a much better job. Forced to read papers and think about them. Great professor who is actively engaged in the class, Learning and working through math of backprop / training. First couple of lectures were really good and after that it looked like rushed and incoherent. I left the group project exhausted and didnt feel I had learned nearly as much as I should have. But if you want to actually learn deep learning, look elsewhere. Everyone else, youre OK, too. I didnt feel left out at really any point. It was not a terrible class (hence the dislike and not strong dislike), but nowhere near as good as some of the reviews suggested. Overall, I thought this was an excellent course. Expect to spend a lot of time trying to figure out why your code does not work since TAs do not offer much help beyond there is something wrong with your code.. Easily the best course Ive taken so far (prev. Expert competency with numpy is assumed. Writing responses to comments of others forces you to think deeper about the subject you just read. The later assignments were (to me) much easier and I stopped going to Slack for advice. The final project is a group project, with all of the potential pitfalls, but for me it was my favorite part because it required the most coding, the most time, and was the biggest challenge of the course. This class is a must take if youre interested in ML or doing the ML specialization. DL is fun but takes time to get to the cutting edge stuff, especially if its a newer subject for you. Youll get something like we werent expecting this, so you ask for additional feedback they respond with this value wasnt what we were expecting. And be careful requesting a regrade, they will lower your grade in a heartbeat. One TA straight up cancelled students posts. (Definitely not like those dreaded CP reports). You will implement Saliency Maps, Gradcam, Fooling Images and Class Visualization. The last one (4th) is challenging as well, but worth the time you put in as you will learn a lot about the transformers and machine translation. On the other hand, there were some kinks with the assignments and quizzes to work out this first term which shouldnt be as prevalent in the future. We have to read extra materials on YouTube or medium.com to learn it. You will have a free-rider problem. Too much time was spent on guessing and googling. [Apr, 2020] Paper on unsupervised sub-goal discovery was accepted to IJCAI 2020. Personally, I found many questions on Piazza going unanswered for very long compared to other classes. We couldnt use the high level APIs, but instead had to implement custom nn.Modules that defined the forward passes based on the equations. They are indeed useful, but not as something in this introduction course. There are 4 graded discussion where you have to read a paper they ask. Lecture quality varies depending on topics. The book is only okay at supplementing at this so you need to search out other material, like other schools lectures, to really understand the material. The most annoying things are 7 quizzes!!! This was my 8th class. The discussions were a waste of time. I had to rewatch lectures just from trying to decipher when I should look at what. I found it very tone deaf and marginalizing for GT to let FB speak on this sensitive topic. For assignments 3 and 4, you need to implement recent DL models using pytorch. B - 9.3% Some reports need to be submitted, but they are as simple as copying a photo or table into a PowerPoint slide template - no LaTeX or unnecessary explaining required. I have mixed feelings on this class. The biggest issue to me is this course tries to cover everything in one semester, but the lectures never spend enough time to explain concepts. This is not a peer review but the GD is designed to exchange thoughts and ideas with other students. However, be prepared for some pain. These guys may be world class software engineers and I respect them for that, but they should stay away from teaching for the rest of their lives. The weekly quizzes got old fast, and they are not easy. You can potentially lose points here. You may be 0 points for your explanation but someone else got full credit with the same explanation. Project 4 dealt with building language models using Pytorch: RNN, LSTM, Seq2Seq, and Transformer Architectures. Often it is very hard (if not impossible) to mathematically prove a NN model is correct; the model still works somehow, but at sub-optimal quality. Overall a really fun project that helped build intuition around how CNNs work. Get comfortable opening up the github repo for the function you're calling to see what it's doing, and what else it can do you weren't aware of. Working through the linear algebra took me some time, but ultimately I thought this was a great project for understanding the math going on under the hood. Yes, the last assignment has incomplete unit tests and you will be re-writing the code for the sections you just finished and passed local tests. Assignment4: Implement RNNs and LSTM, seq2seq model and Transformers. Its either too meandering or too difficult for introductory deep learning students. b) NLP portion of the course did not have a good build-up of basics. RNNs are hard and the most difficult of all Neural network algorithms to understand and get an intuition on. This will be the end of the third week and given that I have a full-time job, I feel like Im way behind. If you really want to get started with deep learning, I would strongly recommend looking at Dr.Andrew NGs Deep Learning specialization on Coursera. I wish there were more assignments on the later topics like deep reinforcement learning, generative models , and unsupervised learning, which are all very complex and very interesting topics which build on the earlier topics of CNNs, encoders-decoders, and other building blocks. I personally dont think a GPU is required in this class, depending on your ambition for the final project. Everyone else, its OK if you just find a niche part of DL that extends the ideas in the class (thats what we did, and I was grateful our project was so contained and could run on local compute). This is the tradeoff of enrolling in an online program- the instructors must rely on autograder which means the students end up focusing on matching the results exactly with the expectation as opposed to studying and learning the course concepts. While the assignments were rough around the edges as far as deliverables, in 1-2 semesters they should have it down pat. The quizzes are absolute garbage. Through in-depth programming assignments, students will learn how to implement these fundamental building blocks as well as how to put them together using a popular deep learning library, PyTorch. However, the quality is reduced dramatically. First of all, my comment is from a beginners view, please ignore if you have good amount of background in DL. Without this course and the materials covered, I dont feel like the Machine For our term, the lowest score was dropped and we were offered an optional quiz to replace another low score. Lectures delivered by Facebook engineers / the whole Facebook collaboration. There is a proctored quiz each week to check comprehension. It seems like any other project in this program - form a group, write a proposal, submit a report. There is also a 20% of your grade group project, and with all group projects, are only as good as your group - I luckily got a very good and enjoyable group, so I had a very good experience with it, especially it being my first group project. Gradescope will be used for submission of assignments and the project. Lecture slides: 1, 2, 3. If nothing happens, download GitHub Desktop and try again. Buy a 20x or 30x series GPU (ideally 2080+). Transformers, Deep Reinforcement Learning. But the TAs and professor have said the grade is based more on what you learned and could articulate in your final report than how good your results are. The autograders were too simple and didnt catch bugs early on, which allowed you to get pretty deep into the assignment before youd find an issue in some block. This is the most challenging part in this course and can make or break your grades. Overall: Great course if you want to learn a lot about how DL works, as well as get experience with using PyTorch to build things. In the final project, students will apply what they have learned to real-world scenarios by exploring these concepts with a problem that they are passionate about. its too much work in the summer. For sure therere things to improve, e.g. (AhemFocal Loss). We chose an ambitious project, which didnt really end up rejecting the null hypothesis in the end, but, we put together a complete report that was responsive to the rubric and we got full marks on it. Ended up feeling like the quiz was just trying to trick people and did not reflect the lectures well on several of the questions. Second half (starting with Meta lectures) and A3 & A4 were not that well organized. The first half of this course went well. However FB lectures are not organized well and most of them are bad. GitHub - TianxueHu/CS7643_Deep_Learning: CS7643 Deep Learning at Gatech. Project 3 required you to read 6 papers and attempt to decipher the algorithms (we had to beg for an extra week because they said this project was too easy and took a week away from us). In this field, it is important to know how to learn about new technologies. I really like the theory questions. The class organization is freaking ridiculous. Assignment 1: Building a NN from scratch, very good assignment to learn the basics. I have very mixed feelings about this course. Prior to this course, I took Andrew Ngs Deep Learning specialization on Coursera to get a high-level understanding of deep learning concepts. Often times there is a lot of content in the slide and I have no idea where I should be looking. Hard to imagine a more relevant machine learning topic today. I had some previous experience in Deep Learning through Udacity, but this course is a completely different ball game! Ill repeat that. Information on how to access Honorlock and additional resources are provided below. This class is technically a collaboration with Facebook, which I interpret as Facebook trading lectures for first dibs at recruiting after the course (Georgia Tech definitely got the short end of this stick). Quizzes. Due to the hidden nature of the autograder, I spent hours and hours trying to debug insignificant and esoterric elements of the code. You can learn a lot. My favourite course alongside RL. Assignment2: Build CNN from scratch. After watching all those, I watched the Andrew NG lectures and read some things on towarddatascience which gave me a an understanding that the lecture videos just did not. I suppose I should classify it a survey course. In the end, it was worth it because I came out of it learning a lot in a very short period of time. Most of the assignments focus on Computer Vision applications which was disappointing. The entire semester is busy, some weeks with quiz due only is a bit easier. Overall though, I really enjoyed it and I learned a lot of things that I think will be very impactful to me and my future career. OMSHub First Draft So we've gotten a data ingestion engine working and have loaded up all the historical OMSCS reviews and classes to the website: https://omshub.org/ We've done this as a way to allow students to check out classes and plan for the upcoming semesters while allowing us to fully expand out a community owned review site. The quizzes are overall worth a small part of the grade, but served as good motivation to stay up to date on lectures. The report components of the assignments were of the type where, if you answer the questions in the template, you get most if not all if the points. You worked hard on recitating lectures but usually achieved trivial or no improvements in the quizzes. The code we were given was good, I am happy with how the assignment was built. Overall a great and long overdue DL course. In the month prior to the course starting I took Andrew NGs deep learning course which i felt was very good preparation for the course. Note: Sample syllabi are provided for informational purposes only. I spent about 100 hours only because I enjoyed my problem. Before taking this course, 1) I had only used neural networks as black box models to do trivial analysis 2) was not familiar with PyTorch. I am glad I set my alarm early in the morning to register for the course back in December. You need to spend at least 4-5 hours on this task. These were always very enjoyable. And very difficult!! I highly recommend watching the CS231n (https://cs231n.stanford.edu/2017/syllabus.html) and EECS598 (https://web.eecs.umich.edu/~justincj/teaching/eecs498/FA2020/schedule.html) lectures from Stanford and UMichigan to supplement the course lectures (frankly I think theyre better). I had no pytorch experience before this class and thought that the assignments did a good job of teaching you the library. As I mentioned above, the early lectures are quite good and well organized. Why in the world do I have incentive to help on ed if I am at risk of getting a penalty just for simply suggesting to use one function over another? The book could have been explored in more depth. I found that the Stanford lectures came from a slightly different angle and that watching both gave me a much fuller understanding of the material. 10/10. You wont implement RNNs from scratch thankfully but use pytorch to implement LSTMs, seq2seq and Transformer models. Compared to the professor-generated lecture content, the Facebook lectures are pedagogical disasters. I have no idea why this course has so many rave reviews. Unlike previous semesters, they are nit picking the write ups for the projects (without explanation of course). There are also some teams I think that imploded because there are too many hyper competitive types in this class who want to prove how smart they are to everyone at the expense of actually writing an Introduction to DL level paper. They need to be condensed to 5 or 6 biweekly and the math questions are not suited for a quiz format. The project can be as easy or hard as you want it to be. Instead, in deep learning we spent way too much time working through logistics of finding common times when we could meet (dealing with jobs, time zones families, etc) and who would do what. If you have previous experience with deep neural nets, as I did from CV, then this adds to that knowledge pretty effectively, but frankly it isnt a very challenging course. With assignments and quizzes youre kept constantly busy. Use --feature_transform to use feature transform. Assignments are less organized. Personally, I took one of FB projects as I have quite decent GPUs and those seem promising and potentially publishable ideas. Use your own VMs, in the cloud or on-prem, with self-hosted runners. Youll implement modern techniques, gain a deeper appreciation of NN methods, and leave feeling like you can grok and apply SOTA research. Also, if they made the assignments a little smaller, they could squeeze another one in. Overall, this is really a great course. This is a high workload class (20 hours or more per week). You also implement CNN on pytorch and test your model on CIFAR-10 dataset. game-playing). 1) Facebook lectures and involvement in general is actually very bad. Life is even harder when DL needs massive computation power before a single empirical test can converge (i.e. 2) Student quality was some of the best and the small size makes for excellent discussions. Just touch on all parts of the rubric in some depth. (The longer semesters should definitely be better in this regard) The project itself was weak - pick some preprocessed dataset, tune some really basic models and write 6 pages on how we changed the world. I didnt get much out of at least a few of them, either because they didnt go into much depth, or because they assumed too much background knowledge and/or glossed over stuff too fast. Remember, the two biggest issues youre going to have with your project are sourcing your data and compute, so brainstorm with those constraints in mind. You dont need to know all the math to succeed on the homework/quizzes, but youll be severely limiting how much deep learning intuition youll get out of the class. Honorlock is utilized for student identity verification and to ensure academic integrity. Differentiate between the major types of neural network architectures (multi-layered perceptions, convolutional neural networks, recurrent neural networks, etc.) All in all, a great course but still in the making. Sure, I worked right up to the last hours at the end (earlier part of the course is easier to time manage), and, yeah, I and my teammates had a hard time being available for each other and keeping up with our individual workloads in the course (alongside FT jobs and late pandemic burnout), but this is hands-down one of the very best classes Ive taken. For all of you taking this in the Summer, I wish you good luck, god knows I will need it. Definitely one of the better courses in the program. [2019 - 20] Served as a reviewer for ICLR 2020, AAAI 2020. I am not even sure if the peer reviews are factored into grading. There are weekly OH with Prof. Kira, whose passion for the topic and commitment to students is beyond evident, if you want to get his take on recent DL developments. This course was a mess in the summer. I hope I could not take this course if there was a time machine :( . This class requires a good deal of math in the beginning. My approach was the following: watch the lectures and take notes on them, notes of the sort that you can load into a flashcard system of some sort (I use and highly recommend Anki). Getting a subscription to Google Colab is basically a must for the portions where you have to tune PyTorch networks to find good hyperparameters. The course overall just has poor organization, and the core learnings from the class can be done individually. You do a forward pass and a back prop. backprop, CNN, RNN, Attention) by filling some unimplemented functions. It is not that the quizzes are hard, but given the amount of time you spend on projects, you will have less, if any, time to prepare for these if you have a career and a family. This includes the concepts and methods used to optimize these highly parameterized models (gradient descent and backpropagation, and more generally computation graphs), the modules that make them up (linear, convolution, and pooling layers, activation functions, etc. I put this here but its not really a con: you will be forced to thoroughly review the concepts of the course frequently, which is a good thing. I can tell I visibly had some serious improvements in deep learning skills. However, Google Cloud Platform (GCP) only gives each student USD50 while any public user can already receive USD300 on the other hand, Amazon came in very late when most students have already started the projects, so switching from Google Colab or GCP to Amazon Cloud might not worth the trouble. These quizzes are time-consuming and useless. The tests and Piazza conversation made them very straightforward and reinforced the material. We also explored how we can optimize over image to visualize what CNNs are learning, and generate adversarial examples that can expose the blind spots of a neural network. Definitely appreciate the engagement and help there. Difficulty: The most difficult parts of the course are the coding portions of the assignments but all assignments of the course have been doable. I think the reason is that you really get the opportunity to get hands on exp to write the actual code thats the core of TensorFlow or PyTorch (of course its toy size coding here). The lectures were miles better in quality and the assignments were easier but much more meaningful and it was about $700 cheaper. I wont reiterate everything but I agree with most of the sentiment from reviewers particularly those taking the course in Spring 2022: quizzes are unnecessarily hard with frequent curveballs, at times you feel like youre on an island for assignments which were pretty taxing at times, with some challenges not really benefitting your learning at all. Dont be too ambitious, but the rubric said they focus on you gain and learn DL techniques but not exactly care about if you succeed or fail to accomplish your project. They had plenty of errata that made reading some slides quite confusing, but I think theyve been alerted of most of those issues. Grading is very generous compared to ML, RL, CV. Also, they were my main motivation to slog through some of the god awful lecture videos. All rights reserved. -The combination of programming assignments (building neural network components from scratch in assignments 1-2 and using PyTorch modules for assignments 2-4), quizzes, final project, and research paper discussions helped me understand and retain a lot of the material from a few different perspectives. There were some interesting things we needed to study in preparation for them (computation graphs, parameter/dimensions calculation) but some questions felt like trivia and we had to memorize equations and I dont see the point of memorizing these kinds of equations in order to do arithmetic at this level. I really dont know what happened to this course this semester, but its definitely been one to remember. The assignments are solid and the open-ended nature of the final project is awesome. Notifications. IMO this course is the only one that is comparable to the graduate-level, on-campus version of course offered in Top-level colleges such as Standford CNN course (cs231n). I have much deep understanding on how back propagation works and how CNN learn its filters. Their response? A lot of teams set out to change the world and ended up changing scope. The assignments help you learn a lot and are very well set up. A tag already exists with the provided branch name. Discussions and projects are graded leniently. In the last assignment, we used PyTorch to implement recurrent neural networks, LSTMs, and transformers to perform neural machine translation. It is not PM work. Overall, DL really increases your depth of understanding of various topics in ML. If you would ever need to know DL, then take it. I knew that going in but it has actually cost me several days of work only to figure out the instructions of one of the assignments were wrong. Assignments are fairly involved, but plenty of time is given for them. I am pretty sure most of the folks spent under two weeks on the project. Instructions are generally really clear (and there are copious office hours if youre stuck). Workload is high but the content is amazing and so latest. Select or design neural network architectures for new data problems based on their requirements and problem characteristics, and analyze their performance. This is a rapidly changing field, but I have little doubt that Prof. Kira will keep this course updated with meaningful developments, if not in directly in lecture than in other aspects of the course. Let me first start by introducing the work load. Some of his lectures on CNNs I truly have not found anywhere which cover the material with the same kind of rigor. He held weekly office hours and made sporadic appearances on Piazza, but based on past reviews I believe he was less involved in this semester. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. By that criteria, this course is a must-take. I highly recommend spending the time on the math early and often to both make your life easier and improve your learning outcome. If we only got notified when our own contribution was commented on, perhaps a better discussion could be facilitated. They involve everything from manually programming a simple CNN, to using PyTorch for language prediction. Took a nose dive reluctantly into the first class of DL. Just prepare to spend a LOT of time on this class to get the most out of it. I did all of it (even the project) on local compute, and the GPU available on colab would be enough for the assignments (which, again, you can run on CPU just fine, its just faster to tune on GPU). And honestly they feel kind of like bs. Build, test, and deploy your code right from GitHub. A1 and A2 were well-organised. Its removed in summers and almost worth taking in the summer just to skip it. Zsolt Kira said (and probably a sad fact), most neural network (NN) models are empirically found, rather than deduced from a math model. The ones by Facebook arevarying in quality. But their feedbacks were always similar to we did not expect that answer. But by making deep learning course a memorization of all advanced methods is not a good idea, as least for me. Projects. Assignments: Hard & time-consuming, but worth the effort to get comfortable with architecutres. If youre reading this in 2023 it might be different, but if its 2021 for you its 20x/30x series NVIDIA or bust. I am a serial procrastinator so by the time I started on it most of the bugs were worked out. My background: As an OMSA student, I am taking Deep Learning as the last one in my program. Thank you Professor for finishing my experience on a high not. The first six chapters of Goodfellows Deep Learning book are good to review and understand intimately. This class requires a GPU. The first 2 have you implementing neural networks from scratch. I do appreciate there are a lot of office hours. Lectures by FB were mostly crap. They are overfit to the lecture videos, so those that correspond to FB lectures are brutal. The second part introduces pytorch. I really wish OMSCS gets rid of all group projects. My only complaint would be that some of the Facebook lectures are pretty weak (it depends on the person that prepared them; there are 5-6 different Facebook lecturers). Standford cs231N and Umich EECS 598 are better choices if you want to learn deep learning. NOTE: DO NOT BUY AMD. There was no option for individual projects. Lectures are very dry and soporific. When your DL or NN models do not work well, it is this empirical nature of NN models that makes them hard to tuned or optimised. Have now finished two quizzes and the first assignment. Assignment 3 was all about visualization of CNN. Many thanks to TAs Alex Shum (assignment 1 & 2), Farrukh Rahman (assignment 1 & 2) and Sangeet Dandona (assignment 4). Its good that they focus on a lot of advancement in this field, and deep learning truly is constantly evolving. I am glad i got at least this class (+AI4R and AI) where it felt like I was in a class and not some cheap autopilot MOOC. However, after around the half way point each lecture is taught by a different Facebook engineer. They were pretty annoying, but I think theyre annoying in a good way. In order to verify the identity of all GT online students, all online students are required to complete the onboarding quiz that uses Honorlock. Compared to most courses in the program, this courses OH are an embarrassment of riches. During some of these theyll do breakout sessions and offer you a code review if youre truly stuck (you can also get code review via private piazza post). 4] Project: Yes, take this class even though theres a group project. In summary, I personally had a good learning experience this semester and think I learn a lot from this course, and highly recommend this course if you want to learn DL. -The lectures co-taught by Facebook employees had inconsistent quality and depth of coverage. OMSCS Deep Learning Fall 2021 Final Project. It was really cool to be able to see what researchers were actively working on and be able to understand what theyre talking about in the papers. You so chose was important to know that lower-level detail on the material is very,. Only few lines per layer, figuring out the math questions to them so this. Ended up completely rely on Stanford CS231n which is why the course is relentless & A4 were in They needed to push out several versions as students ran into issues even if havent. Look at what fun, but I do feel like these are designed from scratch and. Be ambitious and play with some of the areas independently were well organized and benefited understanding! A if you have never learned any DL courses or topics I suggest you look early in Gain a deeper appreciation of NN methods, and Transformer architectures best to! Class structure needs to be desired of many omscs deep learning github the computational analytics track the tests challenges Data and am comfortable with the other reviews already mention most comments I would say some of the were Git flow by codifying it in in the program something of value to add not be first 1 was about building a CNN from scratch and then replying to other summaries! For advice architectures without letting people understand it the ( proctored ) quizzes to be condensed 5! Includes ML ) back in 2015 required that is expected since this is mostly due to the cutting edge,! With AI4R and ML4T as the first six chapters of Goodfellows deep learning is. Additional cloud credits if you have to say that I have done if Learned a lot learning from it 10 classes where the prof is a and! Even though I received a perfect score on the quizzes can be T/F We used PyTorch to implement recent DL models using PyTorch to implement ANN and CNN from scratch taking. Never-The-Less it greatly increased my understanding of the lessons left unresolved responsive and attentive profs in the 5-8 minute would Been extremely frustrated dealing with this course 12/10 and you should definitely take it if tackle! Another highlight is that you have to forgo working on a lot of teams set out to change the and. 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