It allows us to use the TensorFlow saved modal format without any kind of conversion or performance penalties. And we can run larger models than we can do on client side. There are of course some GPU memory limits you might run into if you try and push a gigabyte model over the web to the client device.
Third point, allows you to code in just one language. If you're already using JavaScript, this is a big win. Currently 67% of developers use JavaScript in development already, according to the StackOverflow 2020 survey. And there's a large NPM ecosystem for Node.js with lots of modules and libraries coming out all the time, so great community support, too.
And then fifth point, performance, as we spoke about, we got the same C bindings as the original TensorFlow in Python, which can be used to get parity for inference speeds, and we've got the just-in-time compiler boost for the pre and post-processing if you choose to convert that over to Node.js. So with that, let's wrap up with some resources that you can use to get started, and learn more.
If there's one slide you want to bookmark, let it be this one. Here you can see all the resources you need to get started with TensorFlow.js. Our website at the top there, you can find many resources and tutorials to help you on your way. We've got our models available at tensorflow.org.js.models. I've only shown you three or four today, there's many, many more on there which you can also be using out of the box to get started super fast. We are completely open source, so we're available on GitHub as well, and we encourage contributions back to the project if you are feeling ambitious. We have a Google Group for more advanced technical questions which are group monitors, and of course, we've even got code planning glitch examples to help you get started with boilerplate code to understand how to take data from a webcam and pass it to some of our models.
So, with that, you can get started very, very quickly. Now, if you want to go deeper, I recommend reading deep learning with JavaScript by Manning Productions, and this is written by folk on my team and the TensorFlow team itself. It's a great book, and all you need is some knowledge of JavaScript, it assumes no prior knowledge of machine learning and it's a great resource to go from zero to hero.
And, with that, I encourage you to come join our community. If you check out the made with TFJS hashtag on Twitter or LinkedIn, you'll find hundreds of projects that people are creating every single week around the world, and I can't show them all in the presentation today, but here's just a glimpse of some of the great things that are going on elsewhere in the community. So, my last question for you is what will you make? Here's one final piece of inspiration from a guy from our community in Tokyo, Japan. He is a dancer, but he's used TensorFlow.js to make this cool looking hip-hop video as you can see on the slide. My point is machine learning is now for everyone, and I'm super excited to see how everyone else in the world will start to use machine learning now that it becomes more accessible. Artists, musicians, creatives... Everyone has a chance now to use machine learning, and if you do, please make use of that madewithTF.js hashtag so we can have you featured in our future presentations and blog post write-ups. Thank you very much for listening, and with that feel free to stay in touch. I'm available on Twitter and LinkedIn for further questions and I look forward to talking with you soon.
Comments