Be prepared to spend 20+ hours a week on the homework assignments. I learned a ton from this course. It makes it to where AI/ML is not a black box anymore. You can understand how things are working and how it all comes back to the math.
The lectures are very good. The professor and TAs are very helpful. It is a great course which I would recommend if you are single and have the time.

I first want to mention that I took this class as a UCLA Extension student. I took it because I was bored to death in UCLA Extension's Data Science program and this class didn't disappoint. I would argue this is the best Machine Learning / Deep Learning class I ever took! The class is hard (be prepared to study a lot) but also incredibly rewarding.

This course has a strong focus on understanding the foundations of Deep Learning so that it isn't a black box anymore. It very often touches the mathematical background of Deep Learning so make sure you are familiar enough with calculus and linear algebra before you hop in. You will be working with tensor sized derivatives a lot and assignments are not coding only!

Assignments are hard but manageable. Overall you will be tasked with either mathematically solving for machine learning problems (e.g find optimal parameters for a noisy linear regression) or manually implementing neural networks in an efficient way. It takes time so make sure not to work last minute.

Besides UCLA Extension I'm a french master's student in Data Science (my course credits this year transfer back to France) and the presence of TAs, discussion sessions and a discussion forum was very new to me. I personally went through the course trying to figure out the homeworks on my own with no help. It makes it harder but you can still manage with enough time.

I also want to highlight that Professor Kao does an amazing job at teaching this class. He explains incredibly well, at a good pace, and also answers questions very quickly.

Overall a great course. If you're looking for a rewarding challenge go for it!

This is probably the best introductory class to neural networks I have attended. It is very well structured and everything is built up in a purposeful way. It goes into the mathematical calculations behind deep learning, where the homework is about deriving and implementing the backpropagation algorithm for different neural network layers and functions. There are many opportunities to ask Prof Kao and the TAs questions during lectures and discussions, which they usually answer in meaningful ways.

On the more mixed side, the lectures in class are long (2 x 2 hours per week, not including discussions) and in this quarter, fell a bit behind schedule. As a result, many assignment deadlines and other topics had to be moved around, which could sometimes be confusing. Also worth mentioning is the workload, which is very high. A lot of derivations in the homework will require taking derivatives with respect to matrices, which can lead to tensor-sized derivatives easily. Coding the derivations in a performance-friendly way is also not trivial. As such, there is no shame in asking for help from students and teaching staff and it is generally encouraged, so you do not spend many nights figuring it out alone.

While the material is challenging it is communicated well and I personally feel I have a much better of what is happening when using neural networks from frameworks such as TensorFlow or PyTorch.

This class has the ability to be insanely tough. The homeworks are insanely hard, and the material covered in class is also really difficult. However, they make the midterm pretty easy, but more importantly they grade *SUPER* easily on the midterm, and the final project at the end is leniently graded as well. Hence, this class becomes a pretty enjoyable experience.

This class has a lot of practice, so if you're like me and don't understand something at first, you can try again until you get it. Each TA publishes slides + discussions + worksheets + solutions to test understanding. Then, Kao goes over lecture and has really good slides which explain wtf is going on (they are verbose enough to actually understand them without needing to hear his voice in lecture). Finally, there were many practice problems for the midterm, I think 3 practice midterms were released, which is pretty crazy compared to other classes.

In my opinion, the homeworks are super hard. I know a lot of people resorted to githubbing them, since they seemed impossible at first. I went to the TAs a lot and they basically told me the answers; the hardest part is just manipulating dimensions and stuff to make the homeworks even run.

The homeworks weren't that good, but the midterm was very fair. I didn't have any idea what was going on in the homeworks, yet easily did the midterm.

Ultimately, a good class that isn't that much work unless you don't utilize TAs at all, in which case it becomes super hard. The midterm is fair and I feel like I gained a good amount of experience in deep learning, and now know how to kinda use PyTorch / TensorFlow. And Kao has no accent! So I can actually understand him, I love other CS profs too but sometimes I have no idea what they are saying. 10/10 class but I wish there were more written homework questions instead of just coding all the time.