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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

Classifying two-dimensional clusters

In this section, we are going to implement the logistic regression model using the core API of TensorFlow.js. This means that we will build the model by combining several kernel ops that are provided by the TensorFlow.js core API. You'll come to fully understand how the model works by implementing the algorithm from scratch.

Preparing the dataset

In this experiment, the dataset is a two-dimensional binary of clusters. The points in each cluster have been sampled from the Gaussian distribution. Although the same standard deviation is shared by all the base distributions that generate each cluster, the center of the distribution is shifted to make them separated clusters. For logistic...

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