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Great video. Thanks

Best. Explanation. Ever. You must have put so much time into this video. Thank you for that!

Hi, I liked very much this video. In real world, how this could be implemented? I guess it is using op-amps. Is there some real world example you could show us? Thank you very much

Dear Dave, Looks like you have the perfect graphical view of the threads and cpu usage.. is it possible to get this python code so as I can run on my PC ?

Very good!

im deploying a python API file on amazon ec2 where one function in it utilizes multiprocessing to parallelize some data processing - assigned 2 CPU cores1 Now if this api is deployed and if the api receives 100 concurrent calls will api fail because it exceeded the processor capacity or any other cause to fail???

Thank you so much for this video! I was trying to do a physice simulation where I am animating particle movements... This tutorial made my animation generation more than 10 times faster!

I can't thank you enough!

Thank you for your excellent video. On line 103 of your code, is shows lineL.set_data(t[frame], y[frame]) where frame is a single number from range(N). This produces an error. Please tell us why your code functions. Thank you!

Awesome channel thanks for your effort, i hope you post more videos on comm. systems

Thanks 🙏

David, your video helped me a lot to understand more QAM. What software do you use to make these dynamic graphs? Thank you very much!

кто еще здесь с курса Шибаева по многопоточке )) ?

12:15 .copy() returns a shallow copy, right? You were talking about deepcopies

If we pandas for data analysis task, what should we use multithreading or multiprocessing?

in 2.15: Why A(1- cos) ~ A

dude keep it up

Could you explain how to get the filtering signal phase sync'd with the carrier signal ? Is frequency sync also an issue, because maybe there could be slight differences in the freq of the emitter and the receiver.

Very good explanations of QAM !!

Modulation ka spelling galat hai

one of those hidden gem channels

This is the very best explanation of threading vs multiprocessing that I have ever seen. Well done!

❤this is amazing

really good visualization of the process!

what's a negative amplitude? (10:00)

Such an underrated video, thanks a lot for such an informative series

Were doing 1D finite difference method along the length of the reactor. Can I phone a smart friend? Your future president is getting creamed by the school right now!

All the videos are incredibly good! 💯

Nice video, but they are not clocks; they are counters.

in 8:29 you said that , 2 million function calls across all threads are done , how you computed it , can you explain little more

wow

Which software is used to monitor the activity?

Excellent video! Thank you!

Rust>>>>>>>python Rust is just better, the Austrian painter was right

In summary, Python supports multiprocessing but not multi-threading.

This is helpful ❤

Thanks ❤

Python Removed GIL

Really informative video¡¡ I struggled a bit with the accent and speed but it's really good¡

Where are you getting the information that python threads are managed by the CPython interpreter? That's not impossible, but it would be unusual -- they could implement them much more easily in terms of actual OS-managed threads. All they need to do is to manage the locking and unlocking of the GIL before and after running the user code.

how did you graph the thread usage and work done per thread. Loved your illustrations

I feel like one comment isn't enough to express how great this video is. If i could frame this explantation and hang it in my room i would do so in a heartbeat

I am immensely greatful for this amazing explanation and visualisation of QAM. I have been wrecking my brain for days to understand this topic, but only now i feel like i can finally grasp it

The amount of work done here is unblievable. Thank you so much

One major improvement I've found is taking your CPU intensive Python code and writing it in this language called "C". Joking aside, great video!

Excellent work, very informative! Thanks a ton for your time!

finally all makes sense. thanks for clear explanation

Nice! I am new to multiprocessing, but also appreciate a good visualization. As I was watching the other video that uses these animations I wondered “how did he do that?” And here we are.

amazing explanation! thank you for the video

Nice, but without that getfigax code much of the video lose it sense for a newbie in Python and plotlib