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Machine Learning Algorithms

You're reading from  Machine Learning Algorithms

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781785889622
Pages 360 pages
Edition 1st Edition
Languages
Toc

Table of Contents (22) Chapters close

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. A Gentle Introduction to Machine Learning 2. Important Elements in Machine Learning 3. Feature Selection and Feature Engineering 4. Linear Regression 5. Logistic Regression 6. Naive Bayes 7. Support Vector Machines 8. Decision Trees and Ensemble Learning 9. Clustering Fundamentals 10. Hierarchical Clustering 11. Introduction to Recommendation Systems 12. Introduction to Natural Language Processing 13. Topic Modeling and Sentiment Analysis in NLP 14. A Brief Introduction to Deep Learning and TensorFlow 15. Creating a Machine Learning Architecture

Deep learning at a glance


Deep learning has become very famous in the last few decades, thanks to hundreds of applications that are changing the way we interact with many electronic (and non-electronic) systems. Speech, text, and image recognition; autonomous vehicles; and intelligent bots (just to name a few) are common applications normally based on deep learning models and have outperformed any previous classical approach.

To better understand what a deep architecture is (considering that this is only a brief introduction), we need to step back and talk about standard artificial neural networks.

Artificial neural networks

An artificial neural network (ANN) or simply neural network is a directed structure that connects an input layer with an output one. Normally, all operations are differentiable and the overall vectorial function can be easily written as:

Here:

The adjective "neural" comes from two important elements: the internal structure of a basic computational unit and the interconnections...

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