This is the code repository for Machine Learning with Swift, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language.
We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, 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.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
let bundle = Bundle.main
let assetPath = bundle.url(forResource: "DecisionTree",
withExtension:"mlmodelc")
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
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.
https://packt.link/free-ebook/9781787121515