omscs deep learning github

For the first two quizzes, they provided TA tutorial and some samples questions and solutions which help you to get prepared a lot. I can tell I visibly had some serious improvements in deep learning skills. Are you sure you want to create this branch? [Oct, 2019] There are questions on the quizzes which feel like they come out of nowhere from the lectures and even the papers and unless you have a perfect knowledge of what was in all of those you will miss plenty of points. The link shared on by the colleagues helped to understand challenging concepts from different dimensions. DL is not SWE work. Project: you can propose any subject regarding DL or tackle one of FBs ideas. Regarding GPU, the course organiser is very kind to invite Google and Amazon to offer few cloud computing credits to the students. If nothing happens, download GitHub Desktop and try again. Deep learning is one of the best and most useful courses Ive taken via OMSCS. Deep Learning is a course which I have very mixed feelings on. Need to implement NN from scratch. If you are serious about AI/Machine Learning/Deep Learning, this course is a must do! Instructions are generally really clear (and there are copious office hours if youre stuck). Gradescope will be used for submission of assignments and the project. His lectures were much better. That being said, I learned a fair amount about how pytorch works through these assignments, and I hope to keep that facility with pytorch up going forward. 4] Project: Yes, take this class even though theres a group project. The course content is very good overall. We attempted a novel architecture for our project which didnt perform how we had hoped, but Im glad we gave it a shot. For the project, pay attention to their request of the effort of one assignment per person for the project. Instructions for Assignment4 are so ambiguous. This is obviously a critical hurdle to pass. Instruction: As mentioned repeatedly below, Prof. Kiras lectures are great and communicate useful, interesting rules of thumb about deep learning. I liked the quizzes, they actually tested if you truly had a grasp on the material. A - 70.0% Either put them as part of take home problem sets or provide examples before the quiz. Of course there are many different networks and optimisers and schedulers, but it seems most of them do not involve complex math equations. The assignments are quite challenging, but I learned a lot by doing them. This was my ninth course in OMSCS (btw I have not taken ML, officially a pre-req, but I didnt feel like I was missing any of the content not having taken ML first). You may also be asked to scan the room around you. Earlier on they gave some good examples of computational questions on the quizzes, but later they dont at all. I am part of the OMSA program and dont come from a direct computer science background. The theory portions of the assignments are easy. 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. Overall, this is a great course. But its not all from scratch; after implementing a lot of fundamentals from scratch, the assignments have you use PyTorch to explore more advanced topics. You need to implement back propagation from scratch, then in the last assignment of CNN youll have a chance to work with pytorch. The class organization is freaking ridiculous. I took RL class and it was still too hard to follow. This was my most difficult course so far, despite the fact that Ive worked as a Data Scientist and have studied neural networks before. I felt a little personally conflicted that the class was co-taught by Facebook, as I dont think they use deep learning in an ethical manner. I was skeptical and worried about being the first in the line of battle. I loved this class. I feel they should be redone for future semesters. Team project would be recommended but you can do it alone if you have a thorough idea about your topic. Overall this class gets the job done in explaining practical ML. Quiz materials can come from the lectures, the papers, or frankly related sources. It felt like an on campus class unlike other classes I have taken in OMSCS. Local test code and an online auto grader are provided. Ended up feeling like the quiz was just trying to trick people and did not reflect the lectures well on several of the questions. Pay attention to the assignment write-ups which carry about 30% weight. The amount of effort should be at the level of one homework assignment per group member (1-5 people per group). But they dont go very deep and then later on you are expected to know that lower-level detail on the assignments/quizzes. There was something due every week and so the pace of this course is relentless. Massive disappointment. I dont think the material covered is divided into all the lectures very well. Its still a high-level overview of many areas but they are a recent development in the field, some SOTA. The lectures were pretty good and covered a fair share of breadth and depth. I probably spent about 20 hours a week for the first 6 weeks, but after that, I was able to get things done in about <= 10 hours a week. Assignments take a long time. The assignments in DL have you implement a bunch of fundamental things from scratch including neural networks (forward and backward pass), convolutional neural networks (forward and backward pass). Although I have attempted to study deep learning through MOOCs and hackathons before, this course gave me a deep dive into deep learning I needed to make all the concepts really stick. they talked about beam search, but never mentioned length normalization, then the quiz had a question about length normalization in it. 2) Weekly quizzes are too many and can be useless T/F type questions. This is one of the few classes Ive taken where the professor is actively engaging with the students, even on the Piazza posts that were just discussions rather than post about the lectures/assignments. As others have said, there were some assignments that the TAs were fixing problems with while students were actively working on the project, but thats to be expected in a new course. Average was below 80%. Completely no quality control on Facebook lectures. My favourite course alongside RL. It needs significant rework - assignments need to be made more meaningful, Facebook content needs to be redone, project needs to have a better definition and scope, etc. The only improvement I have found this semester is they no longer test on the Facebook lectures. The downside is the lack of coherence of the material that is being taught. Good assignments and lectures. Hopefully they can replace this content in subsequent years. Note: Sample syllabi are provided for informational purposes only. 3] Graded Discussion: This might feel like busywork to some, and I wont argue with that much. 1 branch 0 tags. I always felt behind and so its been 3 months of stress. Project 2 dealt with building a CNN from scratch also, and then using PyTorch to build a few CNNs and experiment with. These are simply pedagogical disasters. Workload is high but the content is amazing and so latest. There are weekly proctored quizzes. Easily the best course Ive taken so far (prev. 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. The quizzes are mostly jokes with a few math ones that have no business being quizzes. They require understanding of OOP in Python. Oh, and make sure you read the class forums, theyll hide hidden requirements about the projects in there. 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. Hosted runners for every major OS make it easy to build and test all your projects. This is a high workload class (20 hours or more per week). 4 graded discussions counted for 5% of the final grade. Some of the FB lecturers (SWES and PMS) are probably less knowledgeable about DL than the top 10% of the student body taking the class. The lecture itself is very informative and you will get to know all these up-to-date topic and DL techniques. Surprisingly, the slack channel which is usually bustling with activity was relatively quiet. They are difficult but are good for testing your understanding. 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. For example, you might have an assignment (worth 20%) due next Sunday so youd like to start this (previous) weekend, or you can study for the quiz which is due this Sunday which is only worth 4%. images, videos, text, and audio) as well as decision-making tasks (e.g. The first part of the assignment requires implementing a CNN training pipeline from scratch (similar to Assignment 1 except there are some nuances in dealing with the pooling and conv layers). If you are reviewed as never helping, my understanding is that you could earn a 0 (for 20% of the grade) for the final project, despite the project getting a perfect score for the other teammates. There are discussions in the course, but I actually enjoyed the papers they centered around. I averaged 60-70% on quizzes just because they personally took way too much effort to study for and I wanted to use my time elsewhere, so I just watched the lectures once after the first few. I think they got enough feedback that future classes wont have this kind of trouble. Tl;DR: Go buy a desktop on which youd play Crysis at full settings. kansas junk jaunt 2021. xmltv url 2022. It is recommended that students have a strong mathematical background (linear algebra, calculus especially taking partial derivatives, and probabilities & statistics) and at least an introductory course in Machine Learning (e.g. Basic neural network concepts, optimization, CNNs were all covered very well. Work fast with our official CLI. I didnt find anything in particular very difficult, but it is a little overwhelming scheduling-wise. This is a challenging class that covers a lot of ground. This is the one of the few courses with TAs least helpful, may be because I took the course it was first offered in OMSCS. 5] Office Hours: This part really shines. Professor will post a thread about the main topics covered by the up-coming quiz, which, again, varies in helpfulness. The professor and TAs are engaged and quick to interact via Piazza and even in the Slack channel. Find teammates that you get along with and respect, and then, do your best to be a good teammate. Best solution would be to what CV or BD4H does. I will say that it was a prompt to read and think about papers that either extended the lecture materials or were a glimpse into late-breaking developments. Overall, a must take for anybody who wants to learn deep learning! The lectures were miles better in quality and the assignments were easier but much more meaningful and it was about $700 cheaper. In particular there are always overlapping deadlines. that hide the background execution option from time to time, and allocates & shows the CPU / GPU memory incorrectly so that my program crashes on a memory-full error without any early warning. The course is easier than I thought (though still hard) since the core algorithm is nothing but gradient descent. You can learn a lot. The papers were interesting to read but no real discussion was facilitated. We couldnt use the high level APIs, but instead had to implement custom nn.Modules that defined the forward passes based on the equations. Its a little more clunky than having a local GPU, but it certainly does the job. Every single video was beyond terrible and most of them go like this: Self introduction -> throw in some random technical term with absolutely no context -> brag about some cool stuff that Facebook did in this domain -> the end. The lectures were like drinking through a firehose, but I wanted to learn everything and it all seemed important. I do feel like RL is still the superior ML elective - much more math and paper writing in there. EDIT: The grade distribution ended up being: Spring 2022 syllabus and schedule (PDF). Didnt enjoy this section at all, but I like the effort they made, as paper reading is invaluable. Deep learning CS7643 single-handedly changed how I felt about machine learning at OMSCS. Why are these even a thing. I really like the theory questions. I didnt personally get much out of the project, as the way we divvied up the work I didnt get my hands as dirty with the DL work. The first class was super competitive and the class mean was very high. Deep Learning covers over the course of the semester the following material, All the material is quite fascinating and can feel like drinking from a firehose, which is great! However, the quality is reduced dramatically. -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. Often times there is a lot of content in the slide and I have no idea where I should be looking. These will not take much of your time. NOTE: DO NOT BUY AMD. A PDF write-up describing the project in a self . They are overly short, have no build up (they often start speaking about concepts at a deep level without properly introducing them to the students), and are mostly inferior compared to even random Medium posts at explaining similar topics. CN, DBS, DVA, SDP) and the most worth the workload. Quizzes were either hit or miss: the content that we were tested on was either conceptual or applied and the difficulty ranged from too time consuming or could be completed in under five minutes. Instead of spending time doing deep learning youll be spending time pushing your code to expensive AWS instances or wrangling with GCP/Google Colab environments. 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. I did not like the quizzes at all. TAs not helpful. They are overfit to the lecture videos, so those that correspond to FB lectures are brutal. This should not be your first ML class, and self-study (e.g. They will determine your final grade. This is definitely not a summer course. 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. Lectures are very dry and soporific. Zsolt Kira said (and probably a sad fact), most neural network (NN) models are empirically found, rather than deduced from a math model. It has a good introduction on backpropagation and covers quite a bit about how to derive it. I ended up doing that in parallel with this course and it did pretty much everything better than this course. Graded discussions. If you meet 2 or more of the descriptions below, this course will likely to be a rough ride for you. This is the type of course I was hoping all courses in OMSCS would be like when I enrolled. Best to come into the course feeling confident in Python and data structures. There are very many activities to keep up with (several office hours per week, lectures, readings, graded discussions, assignments, quizzes, final project). For the applied quizzes, we had to do calculations on paper that didnt really feel appropriate. 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. 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). First of all, this class has the worst group of TAs Ive seen yet (and yes, that includes ML). 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. I did like the exposure to all the really interesting research papers we had to read for this class and is one of my most valued take-aways from the class. Applications ranging from computer vision to natural language processing, and decision-making (reinforcement learning) will be demonstrated. Code. 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. Other reviews already mention most comments I would write. One of my favorites in the entire program. For example, at the end there are modules on RL and Unsupervised Learning. If you have experience with multivariate calculus (even if youre rusty) and a broad understanding of ML algorithms and challenges, you should take this course. Although the requirements say you should have taken ML, I did not. Overall, according to my experience, this is a fairly organized course, which covers a broad range of topics in DL such as Gradient Decent, CNN, Language Model, Semi-supervised learning, Deep RL and the advanced topics such as GAN. The third and the fourth ones are quite difficult. If youve just been through a couple MOOCs on DL and think you understand NNs and backprop, trust me, you dont. Feels like theres a fire hydrant of material to read and study but one can easily do well without it if you so chose. Its good that they focus on a lot of advancement in this field, and deep learning truly is constantly evolving. Final project.. This stifles any useful discussion basically. That format with bite-sized sections in the 5-8 minute mark would be better to break up the material and have details sink in. An unethical place like FB should also not be teaching an AI Ethics module. Assignment 4: Making an RNN, LSTM, and transformer for NLP. As many students said, quizzes in this course are never quizzes; they are full-blown exams!! You will learn a lot and feel like you have earned it. Quizzes are extremely difficult, testing random facts from lectures and readings. Every assignment is about implementing some DL methods (e.g. At the beginning they arent too bad but very focused on doing math exactly in the style taught. Project 4 dealt with building language models using Pytorch: RNN, LSTM, Seq2Seq, and Transformer Architectures. The syllabus is complete: it covers the basics, the math behind deep learning, ConvNets, sequential models/NLP, attention models, and a little of more advanced topics. A1, and A2 were wonderful experience of doing backprop from scratch, I really liked these. Sometimes the lectures also breeze over the math as if it should be quite clear but to me it wasnt always that way, so be prepared to do some outside studying if you want to really grasp the underlying math. They even completely forgot to grade an assignment until someone asked about it, then they pretty much gave everyone full credit if you turned something in (was only worth 0.5%). They really teach you modern deep learning and the PyTorch parts were so much fun. farm girls pussy. I want to preface this in that I have 5 years experience as data scientist focusing on deep learning, I have published research on deep learning prior to taking this class. I think the questions were very fair on the first 3. It is very math-heavy, especially in the beginning. But if this is the first class of your Deep Learning adventure, maybe its OK. I didnt find the office hours by the TAs very helpful. I would say some of the comments in code were even misleading in a couple spots. Its an unbelievable stress inducer, they test extremely random factoids from the lectures and you pretty much have to know perfectly word for word what is presented on the slides. The first two assignments are pretty good I felt like I learned something from them. Reinforcement Learning lectures by a grad student = WTF. 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. At first you read because you have to, later - because you want to. Group project: It is what you make of it. Time consuming. I rather enjoyed it, but you really want a GPU to test this out and get your losses low enough. The lectures are mediocre (dont even get me started on the Facebook lectures. Assignment 4 was all about RNNs. This class is a must take if youre interested in ML or doing the ML specialization. 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. Buy a 20x or 30x series GPU (ideally 2080+). 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). I learned so much, and so much of it plays right into my work in a computer vision startup where I work in product management in a role that lets me get my hands on our technology. In the first half you will implement large parts of multi-layer perceptrons and CNNs by hand and including back propagation for these models. Not mentioning that Colab UI has issues (bugs?) There is a level of math understanding required that is higher than in other CS courses in the program. (Definitely not like those dreaded CP reports). Another issue is they put a lot on the to-do list to fill up your time, so you might burnout and lose motivation quickly. The above about length normalization is an example of why people may not like the quizzes. I was pleased with this class. Great combination of theory and practice. Prof Kiras lectures are good. most assignments are difficult but manageable if started early and a disaster if started late). The most annoying things are 7 quizzes!!! Grading is SLOW. It might feel a bit painful at first, but I highly recommend writing up a hyperparameter tuning script to save down best configurations and running it overnight. You should give it a try. Project 1 was about building a simple fully connected deep NN from scratch, using no ML libraries. I spent about 100 hours only because I enjoyed my problem. The data itself will run into 100s of GBs and a GPU is a must for most of them. 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. Personally, I took one of FB projects as I have quite decent GPUs and those seem promising and potentially publishable ideas. 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. And very difficult!! I also think more of these assignments should be focused on some of the applications rather than designing the networks from scratch. The same deep fashion project I wasnt even able to properly load in the data, this course helped (or forced lol) me to complete it. Overall I loved this class and think its a must take in addition to ML and RL for the ML specialization. If you want to be ambitious and play with some Facebook problems, you can do that in this course. You know what that means? Also, the textbook was not great and I ended up returning it in 10 days. Very much worth your money, and some hard work. 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. Overall, DL really increases your depth of understanding of various topics in ML. This is my 7th class in the program and I took AI and ML right before DL. Assignment 1 requires implementing the training pipeline (backprop and cost function) for two network architectures. One I couldnt understand half of what the girl from FB was saying (very strong accent). As I mentioned above, the early lectures are quite good and well organized. I liked the assignments. game-playing). There was an assignment that would only pass local tests if you had an Intel CPU. I need to remind myself several times a week that the class is almost over, in order to not melt down in the last two months. The group project is great in theory; however I feel youre not given enough time to do it justice. Hints from Prof.Kira were also helpful. As everyone else has said, the lectures by the professor are impeccable, but the Facebook guest lecturers are pretty trash in comparison they did do enough for me to get through the quizzes and assignment, however. The assignments went through some churn fixing errors as can be expected for a first-run course, but eventually got sorted. The later assignments were (to me) much easier and I stopped going to Slack for advice. One is that there is some roughness-around-the-edges. My background: Handful of years experience programming in C. My previous classes were: Computer Vision, Intro to OS, Advanced OS, Intro to High Performance Computing, Human-Computer Interaction, Graduate Algorithms, and Machine Learning. The annoying parts: Overall, this course can be GREAT. 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. 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. My only real complaint, as stated by other students, is I really disliked the Facebook lectures. You end up doing 75% of the work in 67% of the time. But to this point, I found that the discussions on Slack were invaluable for overcoming errors and learning from others on those harder assignments. TLDR: Great course, demanding workload and conceptual difficulty. Absolutely hated it. Notifications. 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. Using online cloud resource is not very practical. Buy me a coffee 2022 OMSCentral.2022 OMSCentral. Be wared that it is math heavy but I feel like diving into the math really lets you absorb how deep neural networks are working. 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). TAs are very responsive and their office hours are good for getting unstuck. The homeworks are fine, which progressively guide students through the DL concepts, and teach you how to use PyTorch (though it is best to run PyTorch in Colab to avoid rare local CPU errors). Are you sure you want to create this branch? Assignment 4 focused on RNNs and encoder / decoder architectures for the application of language translation. 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.

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