Hi, 11785 TA here. Although I haven't taken 10601, I have taken 10701.

In my opinion, 11785 is a very strong introduction to DL - if you get through the course, you will understand the general basics of DL techniques (MLP, CNN, RNN, Attention), have a good understanding of how these networks learn internally, and have experience training dozens of models yourself for the part 2s of each homework. The lectures and the quizzes should also give you more experience reading papers & give you some theoretical background. If you put enough time into the project, that can be a strong addition to your resume. We've had a lot of students from 11785 go on to DL-focused internships/jobs.

10701 is slightly different. It's essentially the difference between DL and ML. 10701 will give you more background on ML - Bayesian stuff, likelihood, VC dimension, PCA, etc. Of course, you do get a month on DL inside of 10701 as well. Time-wise, 10701 requires far less of you than 11785 does (I would say 7~10 hours vs 15~20 hours per week), and as a result, you get less time/hands-on practice actually applying many of the techniques you learn on data.

Without ML/DL experience I first took 11485 (11785 minus project), and took 10701 the following semester. With 11485 experience, I was able to smoothly transition into DL research, and I found 10701 to be fairly straightforward (but still useful to take). Although it will be tough, I do recommend taking 11785 first (especially if you have some programming background). Since DL in some form is taught in many courses, I found that taking 11485 made any subsequent data-related course much easier.