The first class had only 150 students and were all OMSCS veterans and very motivated. Really a great class. And those were good, whenever I could attend. DL is such an interesting topic, it is unbelievable how boring these lectures are. Pull requests. You never know whether to believe canvas, grade scope, or the syllabus because they all three have different times. Overall a great and long overdue DL course. Theres at least 20-30% of the quiz that is heavy math and thats on all of the quizzes. You have to implement algorithms that will visualize parts of an image that the CNN uses (activations) in its decision making. Its a little more clunky than having a local GPU, but it certainly does the job. Finally, the workload is probably 15-20 hours a week, much like AI sans the crazy exams. Select or design neural network architectures for new data problems based on their requirements and problem characteristics, and analyze their performance. It is a discouraging thing. In this sense, this is an essential class to take if you are in the ML track or are seriously interested in ML. And very difficult!! [Oct, 2019] I enjoyed the first few (say 25%) of the lecture videos. 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). Less content overall compared to the full semesters was a let down. Here you implement ANN or MLP. Nirbhay Modhe But boy I am glad I did. Definitely needs a couple weekends on it at least. Lectures are very dry and soporific. You definitely dont have to. Reading articles is crucial to keeping up with the developments in the field. I see some people complaining about group projects, but if you form a group early and find smart, reliable people (there are plenty), its not going to be an issue. It is important to understand OOP in Python going into the course. Life is even harder when DL needs massive computation power before a single empirical test can converge (i.e. They were proctored, closed-book, closed internet and you couldnt use your calculator. Early on, TAs ran dedicated OH and made preparation worksheets for the calculation questions. -While the quizzes covered a lot of material and really demanded a strong understanding, each one was accompanied by a study guide that included most of the major topics to focus on. Tl;DR: Go buy a desktop on which youd play Crysis at full settings. Overall I did like this course, and the material covered is very good and in-depth. If you use this project time wisely, you will know how to train a real world DL model. It will probably be similar in spring or fall but you would at least have more time to work on the project. Transformers, Deep Reinforcement Learning. Georgia Institute of TechnologyNorth Avenue, Atlanta, GA 30332Phone: 404-894-2000, Application Deadlines, Process and Requirements. In short, this course was a disappointment. They are only worth around 4% each but add up. Final course in the program which is also the one I spent most time vs. other courses (had BDH, CV, ML, AI, DVA, ML4T, AI4R, RL, GA). I have been putting in 20-30 hours/week to finish coding portions of the assignments (there are four assignments total) well ahead of the deadline. 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. 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. 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. The papers were great, the questions were great, but the responding to others part was forced and you just end up making generic comments. The first class was super competitive and the class mean was very high. The graded discussions were a new-to-me course mechanic, and I thought they were an interesting new way to get people to learn material via research papers. Regarding GPU, the course organiser is very kind to invite Google and Amazon to offer few cloud computing credits to the students. Reading the papers and throughly understanding them could be time consuming, but I felt I learned quite a bit. I have no words about the Graded discussions and Final Project. This works well in theory if you have good team mates, but if you have team mates that are unresponsive and dont do their share of the work its awful. If you get auto-assigned to a group, you will probably have a bad time. 4 coding assignments, 7 quizzes, 4 paper reading/discussions, and 1 final project which easily takes 3 weeks of dedicated efforts. 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. -The lectures co-taught by Facebook employees had inconsistent quality and depth of coverage. Group project: It is what you make of it. It is very math-heavy, especially in the beginning. 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. I felt the professor - Dr.Zsolt Kira put in a lot of effort into introducing the fundamentals of deep learning. Overall this class gets the job done in explaining practical ML. I dont know how to describe it, but it is like some of the questions are designed to trick you. The first two assignments are pretty good I felt like I learned something from them. Quizzes are the only difficult part of the course. A - 70.0% Deep learning CS7643 single-handedly changed how I felt about machine learning at OMSCS. 3) Project is a good way to get exposure to a problem you want to learn about. However, if you stop at lectures youre hurting yourself. These are simply pedagogical disasters. I would say it was a bad experience in Fall 2021. The professor was attentive and held office hours. You will learn a lot and feel like you have earned it. For all of you taking this in the Summer, I wish you good luck, god knows I will need it. I hadnt done calculus or linear algebra in ~5 years so I was scrambling to re-learn for the first month of class. To me, team members not attending scheduled meetings and leaving things to the end, was stressful. On the plus side, the grading is very generous, perhaps too much so. Should be a course you take right at the beginning after taking AI so you start your OMSCS journey in ML with a good foundation of what you want to focus on more. Overall, good feelings for this course and I learned a ton. I liked the assignments. 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. Excellent course. 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. As many students said, quizzes in this course are never quizzes; they are full-blown exams!! Great class overall. The professors lectures have a good balance of theory and practice in them, while the Facebook lecturers are pretty much like medium posts. 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). 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. A TA from the current Deep Learning class came across my notes and were somewhat impressed. For example, at the end there are modules on RL and Unsupervised Learning. The quizzes involve a fair amount of preparation (taking detailed notes on lecture videos and spending a few hours or so studying the notes on quiz day). Quizzes are extremely difficult, testing random facts from lectures and readings. As other comments said, some of them expected you understood the concepts that were only covered 2-3 seconds in the FB lectures but contributed 25% of one quiz. Home | OMSCentral Group projects. One major caveat with my review is that there were only 150 seats in this first iteration, so hopefully the quality can be maintained (looks like next term its 500 + 50 for OMSA). To make this worse, I ended up in a bad team - one person who didnt bother to review the work others had done and suggested last minute changes to everything and another person who hardly showed up to meetings or did anything valuable. The last two assignments are basically trying to figure out how to exactly implement the awful instructions that are provided and dont aid a lot in learning. RNNs are hard and the most difficult of all Neural network algorithms to understand and get an intuition on. Overall though it ranked in the middle for me time wise averaging 15 hours/week and I took this with GA which I averaged 20 hrs/week. Its a slog with a lot of things to submit, but 70% of the class gets an A historically. The entire semester is busy, some weeks with quiz due only is a bit easier. Then they just become useless trivia that in no way measures your understanding of the material. Fortunately there isnt too much of it and nearly all the critical lecture material is produced by the professor (and is therefore awesome). They require understanding of OOP in Python. A4, relative to the others, was painful as the test harnesses werent as well-constructed as the others and the instructions were less clear. If you are serious about AI/Machine Learning/Deep Learning, this course is a must do! Read comments of other reviewers about this subject, they are pretty accurate. Not here unfortunately. 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. 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. This is the single most practical course Ive taken in terms of new skills I didnt have before, that I expect to use at work regularly especially for my side hustles. Test your web service and its DB in your workflow by simply adding some docker-compose to your workflow file. I didnt find anything in particular very difficult, but it is a little overwhelming scheduling-wise. The assignments had ambiguity and the instructions were unclear initially, but the TAs worked hard on fixing and making the assignments more clear quickly. 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%). I applaud the individuals at FB who stepped up and recorded lectures for our use, but I cant help but wonder if there might have been a way to better support those lecturers such that the final product were closer to the quality level of the rest of the lectures. You pick one of the 2 papers and post a short review on it and also answer 2 questions on it. The final group project is your time to shine. This is the type of course I was hoping all courses in OMSCS would be like when I enrolled. I found it very tone deaf and marginalizing for GT to let FB speak on this sensitive topic. ), and common neural network architectures (convolutional neural networks, recurrent neural networks, etc.). Also I wish the final projects provided by FB can have more help available on troubleshooting their existing code/libs (we spent 2 weeks just to get their base code run). I highly recommend spending the time on the math early and often to both make your life easier and improve your learning outcome. One interesting application was in an assignment where we had to create custom loss functions and then use PyTorchs built in backprop to optimize over images to do style-transfer. This benefits strongly from having access to an Nvidia GPU. Rating: I strongly recommend this course. TLDR: Great course, demanding workload and conceptual difficulty. -Professor Kira made himself very available with weekly office hours and a lot of support during the semester. TianxueHu / CS7643_Deep_Learning Public. I was pleased with this class. The final project is a group project with no individual option, which I felt was more of a lesson in logistics than it was deep learning. This is my 8th OMSCS class and I think this the most engaged Ive seen a professor. If you can prepare for the assignments by watching the lectures and finish early, there will be some weeks where you wont have that much to do, unless you decide to do all the optional reading, which gives some opportunities to breathe for a bit throughout the semester. Too much time was spent on guessing and googling. Good assignments and lectures. 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. The course is well-designed. The assigned book is OK, and a bit dry, but its fair game for the quizzes. That next level of understanding will take you a long way at work, since DL is sensitive and missing details will be crushing. Piazza was a dumpster fire especially during assignment 4. C - 4.0% Most of the assignments have good unit tests that give you that warm feeling of getting a good grade. The math wasnt super hard, but keeping track of everything when you have hundreds of variables across multiple network layers got complicated. Assignments are decent and are where the core learning happens, but the culture of the class discourages helping others and therefore hinders some of the learning. I would have liked if an additional assignment replaced the group project. Also, if they made the assignments a little smaller, they could squeeze another one in. Overall, this is one of the best courses in the program that I would recommend anyone take, even if youre not doing the ML specialty (I am not doing ML). Most other courses only have the relevant research papers dated 5 to 10 years ago, but the amount of research papers on Deep Learning (DL) are really overwhelming in these few years (since the revival of neural network at approx 10 years ago). Tests and challenges in the course are homework assignments, quizzes, and a project. Quizzes contribute to 20% of the grade. Yes, you will struggle with the code. TAs not helpful. Quizzes. The above about length normalization is an example of why people may not like the quizzes. Assignment 4 focused on RNNs and encoder / decoder architectures for the application of language translation. Information on how to access Honorlock and additional resources are provided below. The Honorlock support team is available 24/7. They were a very stark contrast to the lectures provided by Dr. Zsolt. (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. 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. Excellent course as an intro to DL. Overview Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data.
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