For context, this was my seventh course, having taken AI4R, RL, AI, ML, BD4H, and ML4T beforehand, in that order. The second half gets progressively worse especially with the FB lectures. I came away with a strong and crisp understanding of a lot of concepts within DL, and a better programmer to boot. Do NOT pair this with another class unless youre full-time. There were three of these - You essentially read cutting edge DL papers, answer some subjective questions and respond to others posts on Piazza. backprop, CNN, RNN, Attention) by filling some unimplemented functions. The rest of the TAs are not so great. The TAs (special shout-out to Farrukh, Alex and Sangeet) are excellent, patient, and ran high-quality tutorial sessions. I do appreciate there are a lot of office hours. Regarding GPU, the course organiser is very kind to invite Google and Amazon to offer few cloud computing credits to the students. master. Very much worth your money, and some hard work. But by making deep learning course a memorization of all advanced methods is not a good idea, as least for me. I dont think it is possible to do a research paper type DL project in 10 days unless all team members are already proficient in this field. I have taken ML4T but have no background in DL. Learning pytorch and implementing latest research papers were so much fun. Sometimes felt like a grind to get though 25 minute videos (especially the Facebook ones) where the audio is not always crisp and there is a lot of information covered quickly. I completed a M.S in CS already and have good understanding of ML and with some prior knowledge of DL (Completed part of DL Specialization by Andrew Ng.). Assignments took 55% weights of your final grade. CN, DBS, DVA, SDP) and the most worth the workload. Save time with matrix workflows that simultaneously test across multiple operating systems and versions of your runtime. While Honorlock will not require you to create an account, download software, or schedule an appointment in advance, you will need Google Chrome and download the Honorlock Chrome Extension. In many other classes, youre not really assessed on your understanding of every detail in the lectures, so its fine to just breeze through them not really caring to make sure it all sticks. The Prof. K was very dedicated and organized a lot of OA time with prof himself or tutorials with TA to explain difficult concepts, or overviews for the assignments. You might need GPUs or additional cloud credits if you tackle FB projects as those might involve heavy computation. The weekly quizzes forces you to periodically watch the lectures as they are released at least twice. Its really frustrating being super confident walking into a quiz and feeling utterly defeated when you walk away. The lectures provided by Facebook werent that informative and only provided a really high overview of the topics. Overall, if you do the assignments, discussions, project well and somehow manage 60% in the quizzes (which is not easy), you have a shot at an A. The discussions were a waste of time. It was not a terrible class (hence the dislike and not strong dislike), but nowhere near as good as some of the reviews suggested. Massive disappointment. Even after ML and RL, this course was not easy, but worth it. The final group project is your time to shine. 2) Weekly quizzes are too many and can be useless T/F type questions. He clearly has a passion for DL and teaching it. I think the Deep Learning teaching is facing a very serious problem, in the sense that, people want more students to learn deep learning, so they reduce the difficulty and expand the topics to cover as much as they can. I tuned all my models manually and that was more stress than it was worth. 1) Prof Kira has some of the best lectures and some of the best engagement I have seen in any class. For the applied quizzes, we had to do calculations on paper that didnt really feel appropriate. They were pretty annoying, but I think theyre annoying in a good way. 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. Never-the-less it greatly increased my understanding of the content. Class starts out strong but continues to get worse in every aspect. Personally, I found many questions on Piazza going unanswered for very long compared to other classes. It will probably be similar in spring or fall but you would at least have more time to work on the project. The class structure needs to be done which is pretty pathetic given how new the class is. I really hope they decide to make the group component of this project optional. Quizzes are demanding and require effort and study time to devote and really understand the concept, and first two quizzes includes more computational questions that requires you the calculate the Gradient Decent and input the number for it for example, the later quizzes are more on the conceptual questions but still entails computational questions. This is mostly due to the format of Canvas, which alerts you when anyone in the class comments on anyones response. There is a level of math understanding required that is higher than in other CS courses in the program. We had no final for this class (not sure if that will change), so its really ~5 weeks of uninterrupted group project. And for sequence models, its so bad. In the last assignment, we used PyTorch to implement recurrent neural networks, LSTMs, and transformers to perform neural machine translation. It is possible to get extra credit on Ed by getting endorsed posts, which generally are best done via finding papers about SOTA techniques and/or making detailed notes to help other classmates. The professor, TAs, and content are all top notch. Taken in isolation, 7641 machine learning is at best an okay course and when compared to how other programs and departments teach machine learning, CS7641 really pales in comparison. 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. Sorry Facebook, I really cannot appreciate your video lectures (though I thank you for PyTorch). But if you want to actually learn deep learning, look elsewhere. Cons/Areas for improvement: Grading is SLOW. While the assignments were rough around the edges as far as deliverables, in 1-2 semesters they should have it down pat. If youve just been through a couple MOOCs on DL and think you understand NNs and backprop, trust me, you dont. This is a very hard course. TA and student quality of responses on Piazza was pretty lacking though. I recommend this course. It is a constant barrage of deliverables week after week. Assignments are quite easy compared to other classes (as I remember, they are mostly filling in TODO sections with pytorch code), especially if you have numpy or pytorch experience (since pytorch operations are very similar to numpy), and the assignments are unit tested, so you know at the time of submission what your graded score will be (except possibly by some fluke). Also had a problem where they didnt make the calculator in one of the quizzes obvious, so some folks were able to get some math questions thrown out but not for everyone. It has a good introduction on backpropagation and covers quite a bit about how to derive it. Great assignment content, interesting to see the results of the implementations. The class is front-loaded. I have nothing against accents (I have one myself) but you have to think of the wider audience when the speaker is halfway unintelligible. You will have a very thorough understanding of how neural networks are designed from scratch after taking this class through rigorous programming exercises. For this term you were not allowed to work solo and groups were mandatory. I personally learnt a lot from them. Im not going to go over what was already discussed, but wanted to chime in with a few of my thoughts: However, I wouldnt worry about the quizzes as they are only worth a total of 15% of your grade. He was clearly very invested in his students learning outcomes. If not, but are interested in ML, take this over anything else. The ones by Facebook arevarying in quality. However, the guidance within the pdf and the comments in code left a lot to be desired. Strongly recommend to have an introductory understanding of deep learning. Assignment1: Implement ANN from scratch. Cons: The assignments all had several included unit tests that the TAs wrote along with an autograder. I would say some of the comments in code were even misleading in a couple spots. There are tons of good resources online. 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 benefits strongly from having access to an Nvidia GPU. Quizzes: The complaints are mostly valid, lol. Those words could not have been more accurate. Machine Learning - the course assumes that you have the necessary knowledge regarding concepts taught in the Machine Learning course, although I wouldnt say its a hard requirement. (Not so much all the quizzes and graded discussions.) The TA team was hit and miss, but he was always there to answer questions if it was needed. I highly recommend this course to anyone that is part of the computational analytics track! I have said this many times before but DL is not something you just learn through cheap MOOCs and this class proves why. 4 assignments with only one (A2) I would classify as Hard. The RL one was so briefly done by the Facebook lecturer, it doesnt do the subject any justice. The Faebook lectures start to take over and the whole experience is ruined. Papers, the book, and some further self study are crucial if you want to do DL for real. Disclaimer - I took this class in summer (for the love of OMSCS, dont do this) and I ended up in a bad project team. Its been extremely frustrated dealing with this TA group and has made a hard class unbearable. 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 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. Wish the instructor would re-do some of these lectures, or at least supplement them a bit so that theyd be easier to follow. Overall, a good assignment, although by this point I felt Id done enough implementing from scratch and was excited to start using pytorch. 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. They scheduled mostly useless office hours (during work hours too) and their project involvement included maybe 1 or 2 hours talking to someone who didnt really care much about non PhD students. The biggest problem is that this class is one of the few, if the only one, that actually teaches NLP, and it was poorly covered by a few Facebook researchers who are good at reading off the slides, but not great at actually teaching. The project instructions mandate each students in the project team to work equal share on all tasks, though I still think it would be better for the project if each of us can assume a different role, e.g. This was the only course till now which I never wished to end. Assignments take a long time. You wont implement RNNs from scratch thankfully but use pytorch to implement LSTMs, seq2seq and Transformer models. You can also access Honorlock support at. Luckily they werent worth too much of your final grade, so it doesnt hurt you too much if you bomb a few of them. Being able to read the most important DL papers on your own seems like an essential skill to keep up with this quickly growing field. The FB ones, not so much. To preparebrush up on your matrix calculus skills and check that you have some basic ML skills. If you actually want to learn and understand the material, it will take a lot of time out of your day.
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