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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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Table of Contents (17) Chapters Close

Preface 1. Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js
2. Machine Learning for the Web FREE CHAPTER 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

Asynchronous data access

As a natural consequence of the heavy computation of machine learning algorithms, asynchronous data access is inevitable if we wish to keep our application efficient and working interactively. In JavaScript, asynchronous execution is often implemented with a Promise object. A promise represents an asynchronous operation that ends in either success or failure. Most of the operations that download data from tensors return a Promise object, which ensures that the user fetches the data once it is ready.

To return a Promise object, we need to declare the function as an async. For instance, the Tensor.data method returns a Promise that computes TypedArray, which contains the data's results:

async data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]> {
// Do something to return the value.
// ...
return data as Promise<DataTypeMap...
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