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TensorFlow Machine Learning Projects

You're reading from   TensorFlow Machine Learning Projects Build 13 real-world projects with advanced numerical computations using the Python ecosystem

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789132212
Length 322 pages
Edition 1st Edition
Languages
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Authors (2):
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Ankit Jain Ankit Jain
Author Profile Icon Ankit Jain
Ankit Jain
Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
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Toc

Table of Contents (17) Chapters Close

Preface 1. Overview of TensorFlow and Machine Learning FREE CHAPTER 2. Using Machine Learning to Detect Exoplanets in Outer Space 3. Sentiment Analysis in Your Browser Using TensorFlow.js 4. Digit Classification Using TensorFlow Lite 5. Speech to Text and Topic Extraction Using NLP 6. Predicting Stock Prices using Gaussian Process Regression 7. Credit Card Fraud Detection using Autoencoders 8. Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks 9. Generating Matching Shoe Bags from Shoe Images Using DiscoGANs 10. Classifying Clothing Images using Capsule Networks 11. Making Quality Product Recommendations Using TensorFlow 12. Object Detection at a Large Scale with TensorFlow 13. Generating Book Scripts Using LSTMs 14. Playing Pacman Using Deep Reinforcement Learning 15. What is Next? 16. Other Books You May Enjoy

Collaborative filtering


Collaborative filtering algorithms do not need detailed information about the user or the items. They build models based on user interactions with items such as song listened, item viewed, link clicked, item purchased or video watched. The information generated from the user-item interactions is classified into two categories: implicit feedback and explicit feedback:

  • Explicit feedback information is when the user explicitly assigns a score, such as a rating from 1 to 5 to an item.
  • Implicit feedback information is collected with different kinds of interaction between users and items, for example, view, click, purchase interactions in the Retailrocket dataset that we will use in our example.

Further collaborative filtering algorithms can be either user-based or item-based. In user-based algorithms, interactions between users are focused on to identify similar users. Then the user is recommended items that other similar users have bought or viewed. In item-based algorithms...

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