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Machine Learning Algorithms

You're reading from   Machine Learning Algorithms A reference guide to popular algorithms for data science and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785889622
Length 360 pages
Edition 1st Edition
Languages
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Author (1):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (16) Chapters Close

Preface 1. A Gentle Introduction to Machine Learning FREE CHAPTER 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

A Brief Introduction to Deep Learning and TensorFlow

In this chapter, we're going to briefly introduce deep learning with some examples based on TensorFlow. This topic is quite complex and needs dedicated books; however, our goal is to allow the reader to understand some basic concepts that can be useful before starting a complete course. In the first section, we're presenting the structure of artificial neural networks and how they can be transformed in a complex computational graph with several different layers. In the second one, instead, we're going to introduce the basic concepts concerning TensorFlow and we'll show some examples based on algorithms already discussed in previous chapters. In the last section, we briefly present Keras, a high-level deep learning framework and we build an example of image classification using a convolutional neural network...

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