I have much deep understanding on how back propagation works and how CNN learn its filters. The assignments are the best part and I learned a lot. Assignments are the best part of this course aside from a few I have no idea whats going on moments and having to lean hard into slack / Piazza for direction. I do have a few complaints so far. I had some previous experience in Deep Learning through Udacity, but this course is a completely different ball game! The final project varies, and you are pretty much required to be in a group. They even had a hard time explaining embeddings. Also, the textbook was not great and I ended up returning it in 10 days. I was very lucky to have good teammates, one of whom was so heroic to consolidate all our writings into a big & elegant research paper. The class provided a great split of foundational knowledge/depth and higher level breadth and exposure to deep learning topics. They are closed books and some concepts are very confusing, so I think most students lost most marks on the quizzes, rather than failing an assignments or project. These helped immensely since it can be hard to tell if your code is doing the math correctly. Right, the TAs dont have a clue whats going on with them. I do not have a CS background and enrolled in OMSA for a career change to data science. Are you sure you want to create this branch? Difficulty: The most difficult parts of the course are the coding portions of the assignments but all assignments of the course have been doable. Even after ML and RL, this course was not easy, but worth it. The first two assignments are pretty good I felt like I learned something from them. Build, test, and deploy applications in your language of choice. Thus, its okay to show not good experiment results or discuss your failure experiments as part of your project, but make sure to showcases your understanding through quantitative and qualitative analysis of your experiments. The first 2 have you implementing neural networks from scratch. I dont think the material covered is divided into all the lectures very well. Prof Kira had some good lectures and was active on Piazza. Definitely one of the better courses in the program. That should make the pacing of this course more manageable. First time this course is being done in the Summer. I preferred this approach to the one in AOS in which you have to summarize papers. Hints from Prof.Kira were also helpful. GA Tech's OMSCS is the golden standard for online MS CS programs- so how's UT Austin's newer MSCSO? Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data. FB/meta lectures. A ground up explanation of fundamental NLP would help students who are taking this course to learn from zero as opposed to being already experienced in the topic. Because of the rest of the coursework is graded quite fairly (bordering on generously), this 20% is in practice the differentiator of your letter grade. -There was some uninteresting ambiguity in one of the assignments. I was honestly disappointed by this assignment. Overall, Deep Learning was my favorite course so far due to the content discussed as well as the teaching staff. The readings are interesting and easy enough for someone like me (no STEM background prior to OMSA) to follow. First couple of lectures were really good and after that it looked like rushed and incoherent. 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. This is a very hard course. Lecture quality varies depending on topics. Here is a section wise breakdown along with my ratings: In the beginning of the course, I was delighted that this course had its own dedicated lectures and slides and not some Udacity cut and stitch job. Honorlock is utilized for student identity verification and to ensure academic integrity. TAs not helpful. Machine Learning Spec The machine learning specialization consists of the following courses. As I mentioned above, the early lectures are quite good and well organized. Reading articles is crucial to keeping up with the developments in the field. 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. 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. As a person who watched all the lectures and did assignments of cs231n before, I felt this course is similar to cs231n in terms of high-quality of lectures, the difficulty of assignments, and open-ended projects including FBs ideas which are not trivial but promising. This is easily the worst aspect of the course for me. The coding components of the assignments are auto-graded, which I always prefer to non-autograded coding assignments. You never know whether to believe canvas, grade scope, or the syllabus because they all three have different times. I dont know how to describe it, but it is like some of the questions are designed to trick you. One of the best course in omscs. I highly recommend spending the time on the math early and often to both make your life easier and improve your learning outcome. The TAs and the professor were always very responsive on Piazza. The quizzes were very difficult. On the plus side, the grading is very generous, perhaps too much so. The project is not very difficult except on project management and choice of project objective. I would have liked if an additional assignment replaced the group project. If youre like me, and you are in this program to learn as much as you can, spend the money. The weekly quizzes keep you honest in keeping up with the lectures. If not, but are interested in ML, take this over anything else. The rest of the TAs are not so great. 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. The course is well-designed. This is the third course that I take, with AI4R and ML4T as the first two. I anticipate I will not retain a lot of the info tested on the quizzes, but not so with the assignments. Even with watching all the lectures, taking notes, and doing the readings, I ended up with around average score on the quizzes which sometimes was down around 60%. To preparebrush up on your matrix calculus skills and check that you have some basic ML skills. They tested both lecture and reading material. Its still a high-level overview of many areas but they are a recent development in the field, some SOTA. 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. Applications ranging from computer vision to natural language processing, and decision-making (reinforcement learning) will be demonstrated. The project is a group project, and (no fault of the class) I had a not-so-good group experience, as happens sometimes in OMSCS. Zsolt Kira said (and probably a sad fact), most neural network (NN) models are empirically found, rather than deduced from a math model. They explain the material clearly and, more importantly, they explain the intuition and not regurgitate how to code an equation. 4] Project: Yes, take this class even though theres a group project. Also, if they made the assignments a little smaller, they could squeeze another one in. I am pretty sure most of the folks spent under two weeks on the project. I am glad I set my alarm early in the morning to register for the course back in December. This class will use Canvas to deliver course materials to online students. They are only worth around 4% each but add up. I took RL class and it was still too hard to follow. About my self: This is my 7th course in OMSCS. 4 coding assignments, 7 quizzes, 4 paper reading/discussions, and 1 final project which easily takes 3 weeks of dedicated efforts. A note on effort: GitHub is where people build software. [2019 - 20] Served as a reviewer for ICLR 2020, AAAI 2020. The Honorlock support team is available 24/7. Overall, I thought this was a nice way to connect to the literature in the field of deep learning. Luckily, in this assignment we could leverage the back-prop utility baked into PyTorch, so we didnt need to implement the backward pass. They require understanding of OOP in Python. I am not sure what is the point of them outside of being a grade differentiation. Workload: Varies. Its good that they focus on a lot of advancement in this field, and deep learning truly is constantly evolving. This repo is implementation for PointNet(https://arxiv.org/abs/1612.00593) in pytorch. It felt like an on campus class unlike other classes I have taken in OMSCS. and what types of problems each is appropriate for. Class starts out strong but continues to get worse in every aspect. There were weekly readings, lectures, and quizzes. Overall a really fun project that helped build intuition around how CNNs work. Assignment 1: Building a NN from scratch, very good assignment to learn the basics. Grading is SLOW. Discussions and projects are graded leniently. They graded 200-400 of them in 3 days and were not picky at all. Assignments are less organized. 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. This is my 7th class in the program and I took AI and ML right before DL. More than 83 million people use GitHub to discover, fork, and contribute to over 200 million projects. female body cavity search procedure. Because this course is required for the OMSCS Machine Learning specialization, I don't recommend this specialization; and if you are trying to learn machine learning, I don't recommend . Many of the projects involve PyTorch. I put on average of 20 hours per week but the distribution is not even. 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. I was also surprised about active engagements from both Prof.Kira and TAs. GitHub Actions supports Node.js, Python, Java, Ruby, PHP, Go, Rust, .NET, and more. Ill complete this review when the semester ends, but if youre thinking about signing up for this class, you might want to think twice. The only assignment that I think needs a little tweaking is the final one; it felt a bit more high level vs. the more granular nature of the others. Please lower your expectation and prepare for a lot self-learning. Huge shout out to the professor and TAs for being extremely active on Piazza and willing to make adjustments in this first semester as was deemed appropriate (shifting deadlines, updating assignments, correcting quiz errors). Grades for Assignment 2 and 4, Discussions 2 and 3 were all released only in the last week. 1) Facebook lectures and involvement in general is actually very bad. However FB lectures are not organized well and most of them are bad. 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). I would say it was a bad experience in Fall 2021. Id say the course is a fair amount of work but a bit too easy. In ~67% of the time we needed to do ~75% of the quizzes, discussions and assignments and 100% of the final project. My background: As an OMSA student, I am taking Deep Learning as the last one in my program. Assignment 1 requires implementing the training pipeline (backprop and cost function) for two network architectures. Ideally, you get a good group and have no hiccups, but anticipate for some problems especially during crunch time. Excellent course. While the assignments were rough around the edges as far as deliverables, in 1-2 semesters they should have it down pat. This is my 4th OMSCS course but I took the first 3 (including ML) back in 2015. 11 proctored quizzes spaced every week (there are some off weeks), 15%. The dominant method for achieving this, artificial neural networks, has revolutionized the processing of data (e.g. Best to come into the course feeling confident in Python and data structures. THIS CLASS REQUIRES A GPU. I saw many discussions about people wanting to have a natural language processing class. The assignments all had several included unit tests that the TAs wrote along with an autograder. My one complaint is that it took GT so long. Coming into OMSCS i thought this is what the program would be like. Liked the topics covered. It is overall well-taught and the material is fascinating. In general, going through lecture slides and understand the concepts are necessary for combating quizzes. Completely no quality control on Facebook lectures. I felt lucky that they open this course right on time so I had the chance to take it. Its been extremely frustrated dealing with this TA group and has made a hard class unbearable. The exams are pretty hard, but they dont hurt you as much because they carry less weightage. My team so far has been great and the content pretty interesting. Its very practical and hands-on. As stated above: you will know what deep learning is, why it works, and how to use it. Assignment 4 was all about RNNs. This really makes you learn in detail how these models work under the hood and is already far more detailed than how many other courses treat this part of the material. There were times where I felt like Id never get through an assignment but so far (4/4), I have been able to get things finished (right up to the deadline). All in all, getting scores in the high 90%s on the projects isnt terribly difficult, but it requires doing the programming and then completing the report. Luckily they were worth only 15% of the final grade, with the lowest one dropped. The weekly quizzes forces you to periodically watch the lectures as they are released at least twice. game-playing). The class organization is freaking ridiculous. I found the lectures to be very well done and explained the material well. equivalent to CS 7641). 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. The last 2 are higher level assignments where you use PyTorch to implement different network architectures. The FB ones, not so much. 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. Lectures by FB were mostly crap. Even worse, please do not quiz people with pros and cons of different architectures without letting people understand it. In the second half the lecture quality drops substantially with Facebook engineers delivering most of the lessons. You pick one of the 2 papers and post a short review on it and also answer 2 questions on it. That has a rant section of its own below), half of the assignments were pointless and the group project was just two weeks of frustration with absolutely no meaningful end result. Group projects dont work in this program and the teaching staff made absolutely no effort to try to make it work. Most of these lectures were clearly based on Stanfords CS231 and CS224n content, with many of the slides straight up copied from it, and it would be wiser to seek this original content instead as theyre available for free on the internet. The quizzes were very difficult and did not seem to fit the theme of the course. The first half of this course went well. It is not PM work. Like, what is ResNet, what is their advantage? I would recommend that at least one member of the team has a 1080 ti or better GPU available to run the code locally. 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. Also, they were my main motivation to slog through some of the god awful lecture videos. 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). The ecosystem for deep learning is based around NVIDIA GPUs. But to this point, I found that the discussions on Slack were invaluable for overcoming errors and learning from others on those harder assignments. All in all, a great course but still in the making. 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). Just drop FB lectures and group Final Project. Very little passion in delivery, dry, monotonic. Im sure the prof is a nice guy but he manages to make the lectures extremely boring on such an interesting subject. Would be hard to pair with another course. 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. This is where you truly learn the material. We had no final for this class (not sure if that will change), so its really ~5 weeks of uninterrupted group project. (However, based on past reviews, looks like he was more involved during the longer semesters). Assignments took 55% weights of your final grade. 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. You need to figure out the math for the backward pass by writing the partial derivate on paper and use chain rule as the gradient flows backward.
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