Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

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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Table of Contents (18 chapters)
Title Page
Title Page
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
Getting Started with Machine Learning
Getting Started with Machine Learning
What is AI?
The motivation behind ML
What is ML ?
Applications of ML
Using ML to build smarter iOS applications
Getting to know your data
Choosing a model
Summary
Bibliography
2
Classification – Decision Tree Learning
Classification – Decision Tree Learning
Machine learning toolbox
Prototyping the first machine learning app
IPython notebook crash course
Time to practice
Machine learning for extra-terrestrial life explorers
Loading the dataset
Exploratory data analysis
Data preprocessing
Decision trees everywhere
Training the decision tree classifier
How decision tree learning works
Implementing first machine learning app in Swift
Introducing Core ML
Summary
3
K-Nearest Neighbors Classifier
K-Nearest Neighbors Classifier
Calculating the distance
Using instance-based models for classification and clustering
People motion recognition using inertial sensors
Understanding the KNN algorithm
Recognizing human motion using KNN
Reasoning in high-dimensional spaces
KNN pros
KNN cons
Improving our solution
Summary
Bibliography
4
K-Means Clustering
K-Means Clustering
Unsupervised learning
K-means clustering
Implementing k-means in Swift
Clustering objects on a map
Choosing the number of clusters
K-means clustering – problems
K-means++
Image segmentation using k-means
Summary
5
Association Rule Learning
Association Rule Learning
Seeing association rules
Defining data structures
Using association measures to assess rules
Decomposing the problem
Generating all possible rules
Finding frequent item sets
The Apriori algorithm
Implementing Apriori in Swift
Running Apriori
Running Apriori on real-world data
The pros and cons of Apriori
Building an adaptable user experience
Summary
Bibliography
6
Linear Regression and Gradient Descent
Linear Regression and Gradient Descent
Understanding the regression task
Introducing simple linear regression
Feature scaling
Feature standardization
Implementing multiple linear regression in Swift
Fixing linear regression problems with regularization
Summary
Bibliography
7
Linear Classifier and Logistic Regression
Linear Classifier and Logistic Regression
Revisiting the classification task
Implementing logistic regression in Swift
Predicting user intents
Choosing the regression model for your problem
Bias-variance trade-off
Summary
8
Neural Networks
Neural Networks
What are artificial NNs anyway?
Building the neuron
Building the network
Building a neural layer in Swift
Using neurons to build logical functions
Implementing layers in Swift
Training the network
Basic neural network subroutines (BNNS)
Summary
9
Convolutional Neural Networks
Convolutional Neural Networks
Understanding users emotions
Introducing computer vision problems
Introducing convolutional neural networks
Pooling operation
Convolution operation
Building the network
Loss functions
Training the network
Training the CNN for facial expression recognition
Environment setup
Deep learning frameworks
Loading the data
Splitting the data
Data augmentation
Creating the network
Plotting the network structure
Training the network
Plotting loss
Making predictions
Saving the model in HDF5 format
Converting to Core ML format
Visualizing convolution filters
Deploying CNN to iOS
Summary
Bibliography
10
Natural Language Processing
Natural Language Processing
NLP in the mobile development world
Word Association game
Python NLP libraries
Textual corpuses
Common NLP approaches and subtasks
Distributional semantics hypothesis
Word vector representations
Autoencoder neural networks
Word2Vec
Word2Vec in Gensim
Vector space properties
iOS application
Word2Vec friends and relatives
Where to go from here?
Summary
11
Machine Learning Libraries
Machine Learning Libraries
Machine learning and AI APIs
Libraries
General-purpose machine learning libraries
Inference-only libraries
NLP libraries
Speech recognition
Computer vision
Low-level subroutine libraries
Choosing a deep learning framework
Summary
12
Optimizing Neural Networks for Mobile Devices
Optimizing Neural Networks for Mobile Devices
Delivering perfect user experience
Calculating the size of a convolutional neural network
Lossless compression
Compact CNN architectures
Preventing a neural network from growing big
Lossy compression
An example of the network compression
Summary
Bibliography
13
Best Practices
Best Practices
Mobile machine learning project life cycle
Best practices
Machine learning gremlins
Recommended learning resources
Summary
Index
Index

K-means clustering – problems

Refer to the following for more information about k-means and k-means++:

K-means algorithm suffers from at least two shortcomings:

  • The worst-case time complexity of the algorithm is super polynomial in the input size, meaning that it is not bounded above by any polynomial
  • Standard algorithm can perform arbitrarily poor in comparison to the optimal clustering because it finds only an approximation of the real optimum

Try it out yourself: put four pins on a map, as shown in the following image. After running clustering several times, you may notice that the algorithm often converges to the suboptimal solution:

Figure 4.4: Optimal and non-optimal clustering results on the same dataset

Previous Section
End of Section 7
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