https://github.com/camara94/coursera-tensorflow-js
Welcome. This specialization will teach you how to take machine learnings that you may have trained and deploy them in TensorFlow. Maybe you've trained models in Jupyter Notebook or in your laptop, but how do you take that model and have it be running 24/7, have it serve actual user queries, and create value? This course will teach you how to do all that. Yes. So we'll be taking a look at running your models, for example, in the browser with JavaScript, even be able to run them on your phone. So we're just going to have a lot of fun looking at models, being able to take what you need to do to your model to be able to convert it to run on all these different form factors. For you to be good at machine learning, one of the key skills will be not just the modeling but also the deployment. One of the most exciting deployment scenarios is in JavaScript, so that you can have a neural network, train right there in your web browser, and create an inference right there in your web browser. Yes. So go check it out. We're going to be studying all of that in the next course. So please go on to the next video.
https://github.com/camara94/coursera-tensorflow-js
Last synced: 6 months ago
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Welcome. This specialization will teach you how to take machine learnings that you may have trained and deploy them in TensorFlow. Maybe you've trained models in Jupyter Notebook or in your laptop, but how do you take that model and have it be running 24/7, have it serve actual user queries, and create value? This course will teach you how to do all that. Yes. So we'll be taking a look at running your models, for example, in the browser with JavaScript, even be able to run them on your phone. So we're just going to have a lot of fun looking at models, being able to take what you need to do to your model to be able to convert it to run on all these different form factors. For you to be good at machine learning, one of the key skills will be not just the modeling but also the deployment. One of the most exciting deployment scenarios is in JavaScript, so that you can have a neural network, train right there in your web browser, and create an inference right there in your web browser. Yes. So go check it out. We're going to be studying all of that in the next course. So please go on to the next video.
- Host: GitHub
- URL: https://github.com/camara94/coursera-tensorflow-js
- Owner: camara94
- License: apache-2.0
- Created: 2021-08-21T04:50:42.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-08-24T02:11:40.000Z (about 4 years ago)
- Last Synced: 2025-04-09T15:11:57.712Z (6 months ago)
- Language: Jupyter Notebook
- Size: 2.22 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# coursera-tensorflow-js
Welcome. This specialization will teach you how to take machine learnings that you may have trained and deploy them in TensorFlow. Maybe you've trained models in Jupyter Notebook or in your laptop, but how do you take that model and have it be running 24/7, have it serve actual user queries, and create value? This course will teach you how to do all that. Yes. So we'll be taking a look at running your models, for example, in the browser with JavaScript, even be able to run them on your phone. So we're just going to have a lot of fun looking at models, being able to take what you need to do to your model to be able to convert it to run on all these different form factors. For you to be good at machine learning, one of the key skills will be not just the modeling but also the deployment. One of the most exciting deployment scenarios is in JavaScript, so that you can have a neural network, train right there in your web browser, and create an inference right there in your web browser. Yes. So go check it out. We're going to be studying all of that in the next course. So please go on to the next video.
## Architecture de tensorflow.js
