3) Group project format does not work for DL research work environments in real life. Test your web service and its DB in your workflow by simply adding some docker-compose to your workflow file. It gives a broad overview of many of the deep learning techniques currently being used in industry and research. The quizzes are absolute garbage. One I couldnt understand half of what the girl from FB was saying (very strong accent). Honorlock provides student identity verification via facial and ID photos. Its been extremely frustrated dealing with this TA group and has made a hard class unbearable. The TAs and the professor were always very responsive on Piazza. You have never learned any DL courses or topics. Team project would be recommended but you can do it alone if you have a thorough idea about your topic. During the last month, we actually had 9 Office Hours scheduled with the FB AI researchers in a wide array of topics (Data Wrangling, Transformers, Neural Machine Translation, Language Models, Scaling Deep Learning from Experiment to Production, PyTorch & Scalable Training, Fairness, Accountability and Transparency (FAT), World2Vec). If youve just been through a couple MOOCs on DL and think you understand NNs and backprop, trust me, you dont. Really a great class. Luckily they werent worth too much of your final grade, so it doesnt hurt you too much if you bomb a few of them. The assignments had ambiguity and the instructions were unclear initially, but the TAs worked hard on fixing and making the assignments more clear quickly. Its good that they focus on a lot of advancement in this field, and deep learning truly is constantly evolving. (Definitely not like those dreaded CP reports). I am part of the OMSA program and dont come from a direct computer science background. Fork 10. Excellent course as an intro to DL. The CNN architectures is where things began to fall apart for me. You implement the model and the optimizer. But their feedbacks were always similar to we did not expect that answer. The final group project had a median score of 58/60 (points, not percent). They are very hard though. For assignments 3 and 4, you need to implement recent DL models using pytorch. Its also worth noting that parts of the class were developed by FB AI researchers. But I can say for sure my level of understanding and proficiency in Deep Learning is in a different level now, thanks to this course. My favourite course alongside RL. On the plus side, they were graded leniently. I hadnt been exposed to this side of DLusing backprop to optimize over your input, so it was quite illuminating to me. I think there just isnt a good deep learning textbook yet, which is why the course textbook is so poor. Group project is kind of an annoyance. Quiz: quiz is provided weekly and most of the problems are either conceptual or simple calculations. Forced to read papers and think about them. Never-the-less it greatly increased my understanding of the content. Also, the textbook was not great and I ended up returning it in 10 days. Ohh and the graded discussions are just a waste of everyones time, Overall there is some good material in the class and then it is ruined by the worst structured class Ive had in the program. Having already done Andrew Ngs courses this wasnt a problem personally, but be warned the lectures are not all you need. They dont explain things in decent lecture style detail and it feels as though the presenters are just cursorily describing algorithms and techniques. He was the most active professor that Ive seen on Piazza in the entire program. However, the quality is reduced dramatically. The first half of the course is more math heavy with gradient decent. 3] Graded Discussion: This might feel like busywork to some, and I wont argue with that much. This is mostly due to the format of Canvas, which alerts you when anyone in the class comments on anyones response. Lots of math at the beginning, need to calculate chain rule by hand for backprop, convolutions by hand ..etc. Code is autograded (unlimited submissions) and most of the points; for the written part just make sure you actually offer an explanation of why you think you got what you got. 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 quizzes are overall worth a small part of the grade, but served as good motivation to stay up to date on lectures. While you can technically finish this course without one (I did) and get a good grade, it is way more painful and you wont learn as much. You pick one of the 2 papers and post a short review on it and also answer 2 questions on it. Professor is awesome, TAs are great, course pacing is good, assignments are good, quizzes force you to study, and the final project is well-structured. 4 coding assignments, 7 quizzes, 4 paper reading/discussions, and 1 final project which easily takes 3 weeks of dedicated efforts. 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). Overall I loved this class and think its a must take in addition to ML and RL for the ML specialization. FB/meta lectures. 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. Just run it on Google colab and see if it works. Projects 1 and 2 werent horrible. I apologize if I come across as promoting a different course here, but I was so disappointed that an MOOC can offer so much content that is better in quality than a GaTech course. Its a very busy course, youll have quizzes, assignments, readings almost every week. There was no option for individual projects. Project: you can propose any subject regarding DL or tackle one of FBs ideas. Hopefully they improve that in the future. Comp Photography, AI4R, Software Arch, CV, ML. There are discussions in the course, but I actually enjoyed the papers they centered around. -Professor Kira made himself very available with weekly office hours and a lot of support during the semester. During the first half, I loved it. Massive disappointment. I personally dont think a GPU is required in this class, depending on your ambition for the final project. Project 1 was about building a simple fully connected deep NN from scratch, using no ML libraries. Assignment 3 deals with visualizing network layers and performing style transfer. Were halfway through this semester, but I wanted to go ahead and put a review up to warn others before they sign up for this course. On the plus side, the grading is very generous, perhaps too much so. The FB lectures are not good - too high-level and hand-wavy especially relative to the degree of knowledge that is tested on the quizzes. The first class was super competitive and the class mean was very high. However I felt fourth assignment could use some improvements in instructions. Using online cloud resource is not very practical. I was also surprised about active engagements from both Prof.Kira and TAs. 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). Definitely needs a couple weekends on it at least. I enjoyed the first few (say 25%) of the lecture videos. I have mixed feelings on this class. The grading process is extremely slow and this creates unnecessary anxiety. Im writing this review from the perspective of someone who took it in the Summer, so it may not be indicative of your experience in other semesters. TAs are very responsive and their office hours are good for getting unstuck. (Dr. Andrew NGs specialization does a great job here and teaches you the intent behind each of the architectures with a lot of insights on the implementation). The workload for the class surpassed ML. You can potentially lose points here. Use the piazza as a resource, since often people would share their solutions on it (without codes). This course isnt just a run the model with an ML package type of course. There was a problem preparing your codespace, please try again. The rest of the TAs are not so great. Please lower your expectation and prepare for a lot self-learning. Grading is SLOW. Even if you provide links to YouTube videos on this content, I would be okay with that. Of course there are many different networks and optimisers and schedulers, but it seems most of them do not involve complex math equations. -The course hits the ground running and there is a lot of material to wrap ones head around in the first month. And honestly they feel kind of like bs. Professor shows concern about improving the class. Response time on detailed and thoughtful questions was pretty slow with not always the most insightful reply. The Facebook projects are all real life and hard. kansas junk jaunt 2021. xmltv url 2022. 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. Then they randomly start changing due dates while the assignments are on going. For sure therere things to improve, e.g. Overall a really fun project that helped build intuition around how CNNs work. In general, going through lecture slides and understand the concepts are necessary for combating quizzes. They graded 200-400 of them in 3 days and were not picky at all. Pytorch is nice to finally learn. Automate your software development practices with workflow files embracing the Git flow by codifying it in your repository. This is a high workload class (20 hours or more per week). In BD4H you had over a month to focus on the project, whereas in DL, you have two weeks after the last assignment and quiz to focus just on the project. I always ended up studying the whole weekend and that barely got me to the average Quiz grades. I took this course in Spring 2021, the first semester that it was offered to OMSA students. [Oct, 2019] Actions rgsmoothie/OMSCS-Deep-Learning GitHub That is, an individual lecture is probably fine, but I usually was left thinking that it was not connected very well to the rest of the content. Participation is 5% of your grade, which comes from 3 paper reading discussions - you basically pick a paper, read it, answer some questions posted on Canvas forum, and then have to have two replies to other peers posts. Its removed in summers and almost worth taking in the summer just to skip it. Everyone else, youre OK, too. Group format makes it a pain. At work people would be fired immediately if there was a mismatch of expectations and skills at most places something you cant do in a class format. DL is fun but takes time to get to the cutting edge stuff, especially if its a newer subject for you. Let me first start by introducing the work load. Then they just become useless trivia that in no way measures your understanding of the material. Coding assignments were my favorite, lectures were not very engaging as others stated, especially the meta guest lectures. Definitely one of the better courses in the program. Group project may sound challenging at first, but it is graded very leniently. I find it quite interesting and a lot learning from it. It teaches you the fundamental DL skills, and also the basics of state-of-the-art technologies. Run directly on a VM or inside a container. Feedback to instructors: If you meet 2 or more of the descriptions below, this course will likely to be a rough ride for you. I learned a lot while doing the assignments. ), and common neural network architectures (convolutional neural networks, recurrent neural networks, etc.). It is not PM work. 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. But overall a great course if you manage your expectation/workload well. 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