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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
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

Background of binary classification

Classification is a type of supervised learning. We need a machine learning model in order to predict the correct label for a new instance. For example, the handwritten image recognition problem is categorized as a classification problem. The most popular dataset for handwritten digits is MNIST. MNIST was developed by Yann LeCun, who won the Turing award in 2018 for leading the current boom of artificial intelligence research. This is the prediction result when using TensorFlow.js:

While handwritten digit classification is a multi-label classification problem, the problem we are going to solve in this chapter is binary classification. The target labels to be predicted in the binary classification situation have only two labels: positive and negative. In the following example, there are two classes: a set of rhombuses and circles. If these two...

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