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Machine Learning with Swift

You're reading from   Machine Learning with Swift Artificial Intelligence for iOS

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787121515
Length 378 pages
Edition 1st Edition
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Authors (3):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Oleksandr Baiev Oleksandr Baiev
Author Profile Icon Oleksandr Baiev
Oleksandr Baiev
Alexander Sosnovshchenko Alexander Sosnovshchenko
Author Profile Icon Alexander Sosnovshchenko
Alexander Sosnovshchenko
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Toc

Table of Contents (14) Chapters Close

Preface 1. Getting Started with Machine Learning FREE CHAPTER 2. Classification – Decision Tree Learning 3. K-Nearest Neighbors Classifier 4. K-Means Clustering 5. Association Rule Learning 6. Linear Regression and Gradient Descent 7. Linear Classifier and Logistic Regression 8. Neural Networks 9. Convolutional Neural Networks 10. Natural Language Processing 11. Machine Learning Libraries 12. Optimizing Neural Networks for Mobile Devices 13. Best Practices

iOS application

To use vectors in an iOS application, we must export them in a binary format:

In [47]: 
model.wv.save_word2vec_format(fname='MarkTwain.bin', binary=True) 

This binary contains words and their embedding vectors, all of the same length. The original implementation of Word2Vec was written in C, so I took it and adapted the code for our purpose—to parse the binary file and find closest words to the one that we specify.

Chatbot anatomy

Most chatbots look like reincarnations of console applications: you have a predefined set of commands and the bot produces an output for every command of yours. Someone even joked that Linux includes an awesome chatbot called console. But they don't always ...

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