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.
This book is for iOS developers who wish to create intelligent iOS applications, and data science professionals who are interested in performing machine learning using Swift. Familiarity with some basic Swift programming is all you need to get started with this book.
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 networks 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.
You will need the following software to be able to smoothly sail through this book:
- Homebrew 1.3.8 +
- Python 2.7.x
- pip 9.0.1+
- Virtualenv 15.1.0+
- IPython 5.4.1+
- Jupyter 1.0.0+
- SciPy 0.19.1+
- NumPy 1.13.3+
- Pandas 0.20.2+
- Matplotlib 2.0.2+
- Graphviz 0.8.2+
- pydotplus 2.0.2+
- scikit-learn 0.18.1+
- coremltools 0.6.3+
- Ruby (default macOS version)
- Xcode 9.2+
- Keras 2.0.6+ with TensorFlow 1.1.0+ backend
- keras-vis 0.4.1+
- NumPy 1.13.3+
- NLTK 3.2.4+
- Gensim 2.1.0+
OS required:
- macOS High Sierra 10.13.3+
- iOS 11+ or simulator
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There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The library we are using for datasets loading and manipulation is pandas."
A block of code is set as follows:
let bundle = Bundle.main let assetPath = bundle.url(forResource: "DecisionTree", withExtension:"mlmodelc")
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
let metricsSKLRandomForest = evaluateAccuracy(yVecTest: groundTruth, predictions: predictionsSKLRandomForest)
print(metricsSKLRandomForest) Any command-line input or output is written as follows:
> pip install -U numpy scipy matplotlib ipython jupyter scikit-learn pydotplus coremltoolsBold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "In the interface, the user selects the type of motion he wants to record, and presses the Record button."
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