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Machine Learning with Swift

You're reading from Machine Learning with Swift

Product type Book

Published in Feb 2018

Publisher Packt

ISBN-13 9781787121515

Pages 378 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Authors (3):

Jojo Moolayil

Alexander Sosnovshchenko

Oleksandr Baiev

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Table of Contents (18) Chapters

Title Page

Packt Upsell

Contributors

Preface

Getting Started with Machine Learning

Classification – Decision Tree Learning

K-Nearest Neighbors Classifier

K-Means Clustering

Association Rule Learning

Linear Regression and Gradient Descent

Linear Classifier and Logistic Regression

Neural Networks

Convolutional Neural Networks

Natural Language Processing

Machine Learning Libraries

Optimizing Neural Networks for Mobile Devices

Best Practices

Index

Building a neural layer in Swift

A fully-connected layer is easy to implement, because it can be expressed as two operations:

A matrix multiplication between weights matrix W and input vector x.
A point wise application of activation function f :

Figure 8.5: One layer in detail

In many frameworks, the two operations are separated so that matrix multiplication happens in the fully-connected layer and activation happens in the next nonlinearity layer. This is handy because in this way we can easily replace the weighted sum with convolution. In the next chapter, we will discuss convolutional NNs.

But for now, let's see how NNs can perform logical operations. One neuron is enough to model any logical gate, except XOR. This finding caused the first AI winter in the 1960s; however, XOR is trivial to a model having a network with two layers.

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Authors (3)

Jojo Moolayil

Jojo Moolayil is a data scientist, living in Bengaluru—the silicon valley of India. With over 4 years of industrial experience in Decision Science and IoT, he has worked with industry leaders on high impact and critical projects across multiple verticals. He is currently associated with GE, the pioneer and leader in data science for Industrial IoT. Jojo was born and raised in Pune, India and graduated from University of Pune with a major in information technology engineering. With a vision to solve problems at scale, Jojo found solace in decision science and learnt to solve a variety of problems across multiple industry verticals early in his career. He started his career with Mu Sigma Inc., the world's largest pure play analytics provider where he worked with the leaders of many fortune 50 clients. With the passion to solve increasingly complex problems, Jojo touch based with Internet of Things and found deep interest in the very promising area of consumer and industrial IoT. One of the early enthusiasts to venture into IoT analytics, Jojo converged his learnings from decision science to bring the problem solving frameworks and his learnings from data and decision science to IoT. To cement his foundations in industrial IoT and scale the impact of the problem solving experiments, he joined a fast growing IoT Analytics startup called Flutura based in Bangalore and headquartered in the valley. Flutura focuses exclusively on Industrial IoT and specializes in analytics for M2M data. It is with Flutura, where Jojo reinforced his problem solving skills for M2M and Industrial IoT while working for the world's leading manufacturing giant and lighting solutions providers. His quest for solving problems at scale brought the 'product' dimension in him naturally and soon he also ventured into developing data science products and platforms. After a short stint with Flutura, Jojo moved on to work with the leaders of Industrial IoT, that is, G.E. in Bangalore, where he focused on solving decision science problems for Industrial IoT use cases. As a part of his role in GE, Jojo also focuses on developing data science and decision science products and platforms for Industrial IoT.

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Alexander Sosnovshchenko

Alexander Sosnovshchenko has been working as an iOS software engineer since 2012. Later he made his foray into data science, from the first experiments with mobile machine learning in 2014, to complex deep learning solutions for detecting anomalies in video surveillance data. He lives in Lviv, Ukraine, and has a wife and a daughter.

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Oleksandr Baiev

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