Download Machine Learning with Swift - Artificial Intelligence for iOS - Programming Ebook


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Wednesday, September 5, 2018

Download Machine Learning with Swift - Artificial Intelligence for iOS

Book Details 
             TitleMachine Learning with Swift
         Author: Alexander Sosnovshchenko
    Language: English
        SubjectSwift / Computers & Technology / Programming / Apple Programming
No. of pages: 774
         Format: Epub


Machine learning, as a field, promises to bring increasing intelligence to software by helping us learn and analyze information efficiently and discover certain things that humans cannot. We'll start by developing lasting intuition about the fundamental machine learning concepts in the first section. We'll explore various supervised and unsupervised learning techniques in the second section. Then, the third section, will walk you through deep learning techniques with the help of common real-world cases.
In the last section, we'll dive into hardcore topics such as model compression and GPU acceleration, and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.

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What this book covers

Chapter 1, Getting Started with Machine Learning, teaches the main concepts of machine learning.

Chapter 2, Classification – Decision Tree Learning, builds our first machine learning application.

Chapter 3, K-Nearest Neighbors Classifier, continues exploring classification algorithms, and we learn about instance-based learning algorithms.

Chapter 4, K-Means Clustering, continues with instance-based algorithms, this time focusing on an unsupervised clustering task.

Chapter 5, Association Rule Learning, explores unsupervised learning more deeply. 

Chapter 6, Linear Regression and Gradient Descent, returns to supervised learning, but this time we switch our attention from non-parametric models, such as KNN and k-means, to parametric linear models.

 Chapter 7, Linear Classifier and Logistic Regression, continues by building different, more complex models on top of linear regression: polynomial regression, regularized regression, and logistic regression.

Chapter 8, Neural Networks, implements our first neural network.

Chapter 9, Convolutional Neural Networks, continues NNs, but this time we focus on convolutional NNs, which are especially popular in the computer vision domain.

Chapter 10, Natural Language Processing, explores the amazing world of human natural language. We're also going to use neural net“works to build several chatbots with different personalities.

Chapter 11, Machine Learning Libraries, overviews existing iOS-compatible libraries for machine learning. 

Chapter 12, Optimizing Neural Networks for Mobile Devices, talks about deep neural network deployment on mobile platforms.

Chapter 13, Best Practices, discusses a machine learning app's life cycle, common problems in AI projects, and how to solve them. 

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