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Machine Learning Algorithms - Second Edition

You're reading from  Machine Learning Algorithms - Second Edition

Product type Book
Published in Aug 2018
Publisher Packt
ISBN-13 9781789347999
Pages 522 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (19) Chapters close

Preface 1. A Gentle Introduction to Machine Learning 2. Important Elements in Machine Learning 3. Feature Selection and Feature Engineering 4. Regression Algorithms 5. Linear Classification Algorithms 6. Naive Bayes and Discriminant Analysis 7. Support Vector Machines 8. Decision Trees and Ensemble Learning 9. Clustering Fundamentals 10. Advanced Clustering 11. Hierarchical Clustering 12. Introducing Recommendation Systems 13. Introducing Natural Language Processing 14. Topic Modeling and Sentiment Analysis in NLP 15. Introducing Neural Networks 16. Advanced Deep Learning Models 17. Creating a Machine Learning Architecture 18. Other Books You May Enjoy

A brief introduction to TensorFlow

TensorFlow is a computational framework created by Google and has become one of the most diffused deep learning toolkits. It can work with both CPUs and GPUs and already implements most of the operations and structures required to build and train a complex model. TensorFlow can be installed as a Python package on Linux, macOS, and Windows (with or without GPU support). However, I suggest you follow the instructions provided on the website (the link can be found in the info box at the end of this chapter) to avoid common mistakes and install it in the best way considering every specific environment.

The main concept behind TensorFlow is the computational graph or a set of subsequent operations that transform an input batch into the desired output. In the following diagram, there's a schematic representation of a graph:

Example of simple...
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