Sasaki Hands-On Machine Learning with TensorFlow.js

A guide to building ML applications integrated with web technology using the TensorFlow.js library
1. Auflage 2019
ISBN: 978-1-83882-787-8
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 296 Seiten

No detailed description available for "Hands-On Machine Learning with TensorFlow.js".

Autoren/Hrsg.

Sasaki, Kai

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Digital Lifestyle Internet, E-Mail, Social Media
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen

Inhaltsverzeichnis

Table of Contents - Machine Learning for the Web
- Importing Pre-trained Models into TensorFlow.js
- TensorFlow.js Ecosystem
- Polynomial Regression
- Classification with Logistic Regression
- Unsupervised Learning
- Sequential Data Analysis
- Dimensionality Reduction
- Solving Markov decision problems
- Deploying Machine Learning Applications
- Tuning applications to achieve high performance
- Future Works around TensorFlow.js

Über Autor(innen)

Sasaki Kai:

Kai Sasaki works as a software engineer at Treasure Data. He engages in developing largescale distributed systems to make data valuable. His passion for creating artificial intelligence by processing large-scale data led him to the field of machine learning. He is one of the initial contributors to TensorFlow.js and keeps working to add new operators that are required for new types of machine learning models. Because of his work, he received the Google Open Source Peer Bonus in 2018.