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TensorFlow Deep Learning Projects

You're reading from   TensorFlow Deep Learning Projects 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788398060
Length 320 pages
Edition 1st Edition
Languages
Concepts
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Authors (5):
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Alberto Boschetti Alberto Boschetti
Author Profile Icon Alberto Boschetti
Alberto Boschetti
Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Abhishek Thakur Abhishek Thakur
Author Profile Icon Abhishek Thakur
Abhishek Thakur
Alexey Grigorev Alexey Grigorev
Author Profile Icon Alexey Grigorev
Alexey Grigorev
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Toc

Table of Contents (12) Chapters Close

Preface 1. Recognizing traffic signs using Convnets FREE CHAPTER 2. Annotating Images with Object Detection API 3. Caption Generation for Images 4. Building GANs for Conditional Image Creation 5. Stock Price Prediction with LSTM 6. Create and Train Machine Translation Systems 7. Train and Set up a Chatbot, Able to Discuss Like a Human 8. Detecting Duplicate Quora Questions 9. Building a TensorFlow Recommender System 10. Video Games by Reinforcement Learning 11. Other Books You May Enjoy

Building a TensorFlow Recommender System

A recommender system is an algorithm that makes personalized suggestions to users based on their past interactions with the software. The most famous example is the "customers who bought X also bought Y" type of recommendation on Amazon and other e-commerce websites.

In the past few years, recommender systems have gained a lot of importance: it has become clear for the online businesses that the better the recommendations they give on their websites, the more money they make. This is why today almost every website has a block with personalized recommendations.

In this chapter, we will see how we can use TensorFlow to build our own recommender system.

In particular, we will cover the following topics:

  • Basics of recommender systems
  • Matrix Factorization for recommender systems
  • Bayesian Personalized Ranking
  • Advanced recommender systems...
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