There are a plenty of deep learning toolkits and libraries for different kinds of platforms. For a long time, the three most popular of them were Theano (Python), Torch (Lua), and Caffe (C++). Somehow, Caffe became an industrial standard, while Theano and Torch were mostly used among researchers. I call these three libraries the first generation of deep learning frameworks. Most of the pre-trained neural networks that are available on the internet are still in Caffe format. They had their own problems, so the next generation of frameworks followed in several years. If the first generation was created mainly by efforts of individual researchers, the second generation was pushed by big IT companies. Today, apart from Apple, every internet giant has its own open source deep learning framework: Google has TensorFlow and Keras, Microsoft has CNTK, Facebook released Caffe 2, and Torch was reborn as PyTorch, thanks to Twitter and Facebook. Amazon has chosen MXNet as its...
Machine Learning with Swift
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Machine Learning with Swift
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Overview of this book
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.
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Current Title:
Machine Learning with Swift
Table of Contents (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
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
Customer Reviews