Hands on with TensorFlow.js

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Come check out our workshop which will walk you through 3 common journeys when using TensorFlow.js. We will start with demonstrating how to use one of our pre-made models - super easy to use JS classes to get you working with ML fast. We will then look into how to retrain one of these models in minutes using in browser transfer learning via Teachable Machine and how that can be then used on your own custom website, and finally end with a hello world of writing your own model code from scratch to make a simple linear regression to predict fictional house prices based on their square footage.

This workshop has been presented at ML conf EU 2020, check out the latest edition of this Tech Conference.

FAQ

TensorFlow.js is a library for machine learning in JavaScript, developed by Google. It allows you to define, train, and run machine learning models directly in the browser or on Node.js.

Yes, TensorFlow.js can be used for real-time applications. It supports operations like image and sound recognition, and natural language processing, which can be run directly in the browser, enabling real-time interactions.

While having a good understanding of JavaScript is helpful, you do not need to be an expert to use TensorFlow.js. Basic knowledge of JavaScript and understanding of machine learning concepts are sufficient to get started with using pre-made models. For more advanced usage, such as custom model development, deeper JavaScript proficiency would be beneficial.

Yes, TensorFlow.js can process real-time data from webcams. It can capture video data through the browser, allowing you to perform tasks like object detection, pose estimation, and more in real-time.

TensorFlow.js runs entirely in the client's browser, ensuring that all data, such as images or videos from a webcam, is processed locally. This means the data does not need to be sent to a server, enhancing privacy and data security, especially for sensitive applications.

Yes, TensorFlow.js is compatible with mobile devices. Models can be run directly in mobile browsers, allowing for machine learning tasks to be performed on smartphones and tablets without needing server-side processing.

Yes, TensorFlow.js supports object detection tasks. It can be used to identify and locate objects within images or video streams in real-time, directly in the browser.

The main benefits of using TensorFlow.js include the ability to run machine learning models directly in the browser without server-side dependencies, enhancing user privacy, reducing server costs, and providing real-time analytics and interactions.

Jason Mayes
Jason Mayes
160 min
19 Jul, 2021

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Video Summary and Transcription

The talk emphasizes the benefits and ease of using TensorFlow.js for machine learning directly in the browser or on Node.js. It highlights how TensorFlow.js allows for real-time applications like image and sound recognition, natural language processing, and object detection, making it accessible even to those with basic JavaScript knowledge. The speaker explains how TensorFlow.js can handle privacy and data security by processing data locally in the browser, and is compatible with mobile devices. The video also covers using pre-trained models, transfer learning, and writing custom machine learning code with TensorFlow.js. Examples include face mesh for facial landmark detection, body segmentation, and linear regression for predicting house prices based on square footage. TensorFlow.js can be used for creative projects by artists and musicians, making machine learning more accessible to a wider audience. The talk also provides practical steps for getting started with TensorFlow.js, including using Teachable Machine for quick model training and Glitch for coding and hacking projects.
Available in Español: Prácticas con TensorFlow.js
Video transcription and chapters available for users with access.

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