TensorFlow.js is a game-changer for web developers. It’s like having a genie in a bottle, but for machine learning. You can take pre-made models from Google and make your web app do some serious magic. No need to send data to the server – it all happens right in the browser. With TensorFlow.js, the possibilities are endless, and your apps can now be smarter than ever! π§ββοΈπ
Overview π©βπ»
Welcome to the first video of the Tensorflow.js series! In this series, we will explore the basics of Tensorflow.js, including what Tensorflow.js is, how to use pre-made models provided by Google, retraining existing models with our own data, and building our own machine learning model using TensorFlow.js.
Origins of Tensorflow
Tensorflow was developed by the Innovative Minds at Google brain in 2015 for their internal research and production needs. The official release marked a significant milestone in the machine learning field.
Introduction to Tensorflow.js
The release of Deep Learning JS in 2017 paved the way for running machine learning libraries directly in the browser using JavaScript. This resulted in the birth of Tensorflow.js in 2018, which was an immediate hit. It allowed developers to easily integrate machine learning into web applications and make use of client-side resources, eliminating the need to send data to the server.
Exploring Tensorflow.js π
Benefits of Tensorflow.js
Tensorflow.js enables developers to incorporate machine learning libraries into both browser and server-side applications using Node.js. Additionally, it provides access to pre-trained models for images, pose estimation, audio, speech command, text, and general utility libraries.
Hands-On Programming
In this series, we will engage in hands-on programming and build simple projects using machine learning, including the use of pre-trained models such as Coco SSD.
Leveraging Machine Learning π
Smart Application Development
By the end of this series, developers will be able to make their applications smarter by utilizing machine learning libraries and pre-trained models provided by Google. This includes the ability to retrain existing models or build custom models using Tensorflow, consequently making applications more intelligent. Thank you for joining us on this exciting journey!
Key Takeaways
- Tensorflow.js allows for the integration of machine learning into web applications.
- The release of Deep Learning JS in 2017 led to the birth of Tensorflow.js in 2018.
- Tensorflow.js enables the building of custom models and retraining existing models for smarter applications.
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