- The algorithm is fast for training but slow for inference.
- You need to choose the best k somehow (see Choosing a good k section).
- With the small values of k, the model can be badly affected by outliers; in other words, it's prone to overfitting.
- You need to choose a distance metric. For usual real value features, one can choose among many available options (see Calculating the distance section) resulting in different closest neighbors. The metric used by default in many machine learning packages is the Euclidean distance; however, this choice is nothing more than a tradition and for many applications is not the optimal.
- Model size grows with the new data incorporated.
- What should we do if there are several identical samples with different labels? In this case, the result can be different depending on the order in which samples are stored.
- The model suffers from the curse of dimensionality.
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