How to pronounce“tensorflow”in English

tensorflow came out of the c-plus plus library uh jeff dean wrote i think that was uh basically uh how they were doing inference right and

tensorflow's bolt on i don't mean to offend but it was so bad yeah it's the first time that i i i'm usually so i mean

tensorflow is a good example but you should not pip install tensorflow it's a terrible idea people do it because the popularity of pip many people think

tensorflow and then pie torch on the Facebook side and what you see is there's a magic place where you can find a community

with TensorFlow Quantum. One of my co-founders of TensorFlow Quantum is the CTO, Trevor McCourt.

the tensorflow team and the pi torch team and talk to them after uh after exiting anaconda just talking about because

with TensorFlow Lite.

And from that came TensorFlow, which Google gifted to the world, and it's one of the greatest gifts ever given to humanity.

And so where TensorFlow actually has amazing power in terms of scale and deployment and things like that, and I think Google is, I mean, maybe not unmatched,

and goes underneath TensorFlow and PyTorch.

the research library tensorflow is definitely used and you know they have to make they couldn't just open it up and let the community you know change

is um Swift for tensorflow for example and one of the inspiring things for me

with both tensorflow and pytorch is how quickly the community can like switch from different libraries y like you could see some of the communi switching

let's use tensorflow playground to see what kind of neural network can learn to partition the space for the binary classification problem

It took ten days for TensorFlow to be a part of the decision-making in almost one in four trades that happens every day in the US

What lessons have you learned from TensorFlow Quantum, maybe you can speak to like what it takes to create essentially what,

And so what happened with both TensorFlow and PyTorch is that the explosion of innovation in AI has led to, it's not just about matrix implication and convolution.

And so I think that TensorFlow and PyTorch and these systems embracing Python really took and helped Python grow, but I think that the major thing underlying it is

- So if you look at just TensorFlow and PyTorch, which is pretty recent history in the big picture, right, but TensorFlow is all about graphs.

Now the challenge with what the TensorFlow versus the PyTorch design points was that TensorFlow's kinda difficult to use for researchers,

- And so if you go back to TensorFlow 1, PyTorch 1, this kinda timeframe, and these have all evolved and gone way more complicated.

And so what happened with both TensorFlow and PyTorch is that the explosion of innovation in AI has led to, it's not just about matrix multiplication and convolution.

One of which was this Swift for TensorFlow project, right?

- Swift because it's not Python and TensorFlow because it- - It wasn't set up for eager mode at the time, yeah.

before then it became already before tensorflow before all of that there was a growing realization that c plus is not the right tool for machine learning we

make a big difference we have tensorflow versus pytorch in the machine learning we have the same problem today yes i wonder it's actually bigger i wonder

programs is pie torch now by torch tensorflow there's a couple others the pi torch is one over tensor flows it's just i'm not an expert on that

Like today I'm working on TensorFlow Lite on a Samsung Galaxy S20, doing some deep learning on the smartphone.

Today I'm gonna continue with the TensorFlow Lite work on the Galaxy S20 for the probably entirety of the four-hour session.

So today I didn't finish the TensorFlow Lite thing, but I'm close.

now embracing of uh you know learning tensorflow by torch right there they they see the power of uh trying to

computer first you know implemented tensorflow but yeah I think that in terms of the in terms of the point that you're getting and I do

domain-specific languages the languages like tensorflow and pi torch are very

I remember my colleagues were in my office when TensorFlow, the day it was announced, they were so excited about this, this is going to change the world.

I mean Google is a pioneer in that with TensorFlow in the AI space.

- Yeah, so TensorFlow Quantum was an attempt at, well, I mean I guess we succeeded at combining deep learning

And so if you look at something like PyTorch or TensorFlow or, I mean, even a simple use case, like you define a class that has the plus method, right,

- It's really interesting to me because what PyTorch and what TensorFlow and all these frameworks are kinda pushing compute into is they're pushing into, like,

And because of that and because of things like TensorFlow and PyTorch, we're able to say, okay, we'll express the math of the system.

And the reason for this is that if you look into TensorFlow or PyTorch, these systems, they weren't really designed for this world, right?

It's a better experience that helps lift TensorFlow and PyTorch and make them even better.

And in 2017, TensorFlow's, like, really taking off and doing incredible things.

except for the Swift and except for the TensorFlow part.

learning community so packages like uh Pi torch tensorflow second learn and even like the lower level stuff like numpy sci-fi pandas matplotlib with

with python as the user interflow interface right but with tensorflow

on there for testing purposes it's using tensorflow lite and the new tensor chip

you have pi torch and other library like tensorflow that basically tried to mimic uh yeah

then they realized oh we could do this tensorflow thing that close library then what was interesting to me was the fact that both

universally agreed upon that the bolt-ons on tensorflow right but i went through it there was a talk given at a mallorca in in spain and it got a great