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Hands-On Machine Learning with TensorFlow.js

You're reading from   Hands-On Machine Learning with TensorFlow.js A guide to building ML applications integrated with web technology using the TensorFlow.js library

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781838821739
Length 296 pages
Edition 1st Edition
Languages
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Author (1):
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Kai Sasaki Kai Sasaki
Author Profile Icon Kai Sasaki
Kai Sasaki
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js FREE CHAPTER
2. Machine Learning for the Web 3. Importing Pretrained Models into TensorFlow.js 4. TensorFlow.js Ecosystem 5. Section 2: Real-World Applications of TensorFlow.js
6. Polynomial Regression 7. Classification with Logistic Regression 8. Unsupervised Learning 9. Sequential Data Analysis 10. Dimensionality Reduction 11. Solving the Markov Decision Process 12. Section 3: Productionizing Machine Learning Applications with TensorFlow.js
13. Deploying Machine Learning Applications 14. Tuning Applications to Achieve High Performance 15. Future Work Around TensorFlow.js 16. Other Books You May Enjoy

Using existing models

The TensorFlow.js project has its own repository to collect machine learning models that are compatible with the latest in TensorFlow.js. The repository is named tfjs-models: https://github.com/tensorflow/tfjs-models.

These models are pre-trained and uploaded in npm, unpkg, or jsDelivr so that we can quickly use the model in our application. As you may already know, npm is a package manager for the Node.js environment. It is easy to install the model in your Node.js application by using the npm or yarn CLI. For web applications, unpkg is a good option. It is an open source project providing a global CDN that is designed to serve every npm package. tfjs-models and TensorFlow.js core are served via unpkg. jsDelivr is a similar application that serves static content on our behalf.

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