Why GPUs Outpace CPUs?

[@vitor-ce2ql]

Hello, I recently discovered your Channel, I thought it was very good, and you are very kind, and you have a good explanation, congratulations, I want your channel to grow ,If my English is bad, sorry, because I'm Brazilian

[@khangvutien2538]

At 7:05, I see in the specs sheet 256 Tensor cores. Are they the same tensor processing as in TPU? Maybe you can also explain TPU? Note that I’m just starting to watch. Maybe you will explain later in the video?

[@DigitalSreeni]

The tensor cores in GPUs and TPUs involve tensor processing but they are different technologies designed for different purposes. GPUs are more general-purpose and versatile, suitable for a range of tasks like gaming, graphics rendering, and parallel computing workloads. TPUs are purpose-built for machine learning and are highly optimized for tensor operations.

[@Ayzal.Y_Liebe]

Sir what type of laptop should i get to do deep learning?what do you recommend?

[@anshagarwal9826]

@DigitalSreeni hi can you explain why you divide array size by time to calculate FLOPS how does it give what floating point operations per second it took, what I understood from your calculation is that you might be considering an estimation like how much time it will take to build the newly calculated array is ~ saying FLOPs.

[@DigitalSreeni]

The calculation of FLOPS in my code is based on the time taken to perform a specific operation (e.g., DAXPY) on arrays of a given size. The rationale behind this calculation is that it estimates the rate at which floating-point operations are executed per second. If you consider the DAXPY operation (A * X + Y), each element in the arrays X and Y undergoes a multiplication and an addition, which are floating-point operations. So the total number of floating-point operations is proportional to the array size. It provides a rough measure of the performance in terms of floating-point operations per second. In reality, the actual number depends on the type of operations and of course the underlying hardware.

[@anshagarwal9826]

Thanks ​@@DigitalSreenimuch appreciated 👍

[@alihajikaram8004]

Hi, I found your channel very informative and thanks for your great educational videos. Would you make a video about using conv1d in time series? Could we use it for feature extraction?

[@msaoc22]

thank you for amazing video and time you spend on us=)

[@aaalexlit]

Awesome as always, thank you! any chance to have a follow-up that includes TPUs?

[@scrambledeggsandcrispybaco2070]

Hi DigitalSreeni, I have been using your tutorials as a guideline for segmentation using traditional machine learning. Apeer has changed a lot since your videos were made. When I export the file it gives masks for different classes separately. What can I do ? Thank you for all your knowledge, you are a life safer.

[@hamidgholami2683]

Hi sir hope your doing well May i ask you to make some videos relating to instance segmentation ? I mean a good explanations and also doing some projects based on that? I will be happy if you respond

[@zainulabideen_1]

Found Amazing information, thanks ❤❤❤

[@DigitalSreeni]

Glad it was helpful!

[@vidyasvidhyalaya]

Sir please upload a seperate video for convert this "195 - Image classification using XGBoost and VGG16 imagenet as feature extractor" into loal web application sir...please sir....dont skip my comment sir...please sir...awaiting to see the video sir

[@tektronix475]

I got 5k times faster for the T4 GPU setup for a 10000 matrix size. which is disheartening and eye popping at the same time.

[@fergalhennessy775]

w rizz