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Hands-On Machine Learning with IBM Watson

You're reading from  Hands-On Machine Learning with IBM Watson

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789611854
Pages 288 pages
Edition 1st Edition
Languages
Author (1):
James D. Miller James D. Miller
Profile icon James D. Miller
Toc

Table of Contents (15) Chapters close

Preface 1. Section 1: Introduction and Foundation
2. Introduction to IBM Cloud 3. Feature Extraction - A Bag of Tricks 4. Supervised Machine Learning Models for Your Data 5. Implementing Unsupervised Algorithms 6. Section 2: Tools and Ingredients for Machine Learning in IBM Cloud
7. Machine Learning Workouts on IBM Cloud 8. Using Spark with IBM Watson Studio 9. Deep Learning Using TensorFlow on the IBM Cloud 10. Section 3: Real-Life Complete Case Studies
11. Creating a Facial Expression Platform on IBM Cloud 12. The Automated Classification of Lithofacies Formation Using ML 13. Building a Cloud-Based Multibiometric Identity Authentication Platform 14. Another Book You May Enjoy

TensorFlow basics

Tensors can be thought to be generalized matrixes or, more specifically, mathematical entities living in structures and interacting with other mathematical entities. If the other entities in the structure are transformed in any way, then the tensor must also be transformed by that transformation rule.

What does the preceding definition mean? Perhaps thinking of a Tensor as multidimensional array is easier to grasp, or consider the following, comparing Scalar, Vector, Matrix, and Tensor:

Building on the topic of TensorFlow, TensorFlow is an open source software library (also called a framework) originally created by Google for creating deep learning models.

You can visit https://www.tensorflow.org/ for more details on TensorFlow.

In the next section, we will talk about the connection between deep learning, neural networks, and TensorFlow.

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