Name: Machine Learning with Swift
Author: Alexander Sosnovshchenko
ISBN: 9781787123526

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Machine Learning with Swift

Alexander Sosnovshchenko

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Title Page
Packt Upsell
Why subscribe?
PacktPub.com
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Getting Started with Machine Learning
What is AI?
The motivation behind ML
What is ML ?
Applications of ML
Digital signal processing (DSP)
Computer vision
Natural language processing (NLP)
Other applications of ML
Using ML to build smarter iOS applications
Getting to know your data
Features
Types of features
Choosing a good set of features
Getting the dataset
Data preprocessing
Choosing a model
Types of ML algorithms
Supervised learning
Unsupervised learning
Reinforcement learning
Mathematical optimization – how learning works
Mobile versus server-side ML
Understanding mobile platform limitations
Summary
Bibliography
Classification – Decision Tree Learning
Machine learning toolbox
Prototyping the first machine learning app
Tools
Setting up a machine learning environment
IPython notebook crash course
Time to practice
Machine learning for extra-terrestrial life explorers
Loading the dataset
Exploratory data analysis
Data preprocessing
Converting categorical variables
Separating features from labels
One-hot encoding
Splitting the data
Decision trees everywhere
Training the decision tree classifier
Tree visualization
Making predictions
Evaluating accuracy
Tuning hyperparameters
Understanding model capacity trade-offs
How decision tree learning works
Building a tree automatically from data
Combinatorial entropy
Evaluating performance of the model with data
Precision, recall, and F1-score
K-fold cross-validation
Confusion matrix
Implementing first machine learning app in Swift
Introducing Core ML
Core ML features
Exporting the model for iOS
Ensemble learning random forest
Training the random forest
Random forest accuracy evaluation
Importing the Core ML model into an iOS project
Evaluating performance of the model on iOS
Calculating the confusion matrix
Decision tree learning pros and cons
Summary
K-Nearest Neighbors Classifier
Calculating the distance
DTW
Implementing DTW in Swift
Using instance-based models for classification and clustering
People motion recognition using inertial sensors
Understanding the KNN algorithm
Implementing KNN in Swift
Recognizing human motion using KNN
Cold start problem
Balanced dataset
Choosing a good k
Reasoning in high-dimensional spaces
KNN pros
KNN cons
Improving our solution
Probabilistic interpretation
More data sources
Smarter time series chunking
Hardware acceleration
Trees to speed up the inference
Utilizing state transitions
Summary
Bibliography
K-Means Clustering
Unsupervised learning
K-means clustering
Implementing k-means in Swift
Update step
Assignment step
Clustering objects on a map
Choosing the number of clusters
K-means clustering – problems
K-means++
Image segmentation using k-means
Summary
Association Rule Learning
Seeing association rules
Defining data structures
Using association measures to assess rules
Supporting association measures
Confidence association measures
Lift association measures
Conviction association measures
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
Linear Regression and Gradient Descent
Understanding the regression task
Introducing simple linear regression
Fitting a regression line using the least squares method
Where to use GD and normal equation
Using gradient descent for function minimization
Forecasting the future with simple linear regression
Feature scaling
Feature standardization
Multiple linear regression
Implementing multiple linear regression in Swift
Gradient descent for multiple linear regression
Training multiple regression
Linear algebra operations
Feature-wise standardization
Normal equation for multiple linear regression
Understanding and overcoming the limitations of linear regression
Fixing linear regression problems with regularization
Ridge regression and Tikhonov regularization
LASSO regression
ElasticNet regression
Summary
Bibliography
Linear Classifier and Logistic Regression
Revisiting the classification task
Linear classifier
Logistic regression
Implementing logistic regression in Swift
The prediction part of logistic regression
Training the logistic regression
Cost function
Predicting user intents
Handling dates
Choosing the regression model for your problem
Bias-variance trade-off
Summary
Neural Networks
What are artificial NNs anyway?
Building the neuron
Non-linearity function
Step-like activation functions
Rectifier-like activation functions
Building the network
Building a neural layer in Swift
Using neurons to build logical functions
Implementing layers in Swift
Training the network
Vanishing gradient problem
Seeing biological analogies
Basic neural network subroutines (BNNS)
BNNS example
Summary
Convolutional Neural Networks
Understanding users emotions
Introducing computer vision problems
Introducing convolutional neural networks
Pooling operation
Convolution operation
Convolutions in CNNs
Building the network
Input layer
Convolutional layer
Fully-connected layers
Nonlinearity layers
Pooling layer
Regularization layers
Dropout
Batch normalization
Loss functions
Training the network
Training the CNN for facial expression recognition
Environment setup
Deep learning frameworks
Keras
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
Natural Language Processing
NLP in the mobile development world
Word Association game
Python NLP libraries
Textual corpuses
Common NLP approaches and subtasks
Tokenization
Stemming
Lemmatization
Part-of-speech (POS) tagging
Named entity recognition (NER)
Removing stop words and punctuation
Distributional semantics hypothesis
Word vector representations
Autoencoder neural networks
Word2Vec
Word2Vec in Gensim
Vector space properties
iOS application
Chatbot anatomy
Voice input
NSLinguisticTagger and friends
Word2Vec on iOS
Text-to-speech output
UIReferenceLibraryViewController
Putting it all together
Word2Vec friends and relatives
Where to go from here?
Summary
Machine Learning Libraries
Machine learning and AI APIs
Libraries
General-purpose machine learning libraries
AIToolbox
BrainCore
Caffe
Caffe2
dlib
FANN
LearnKit
MLKit
Multilinear-math
MXNet
Shark
TensorFlow
tiny-dnn
Torch
YCML
Inference-only libraries
Keras
LibSVM
Scikit-learn
XGBoost
NLP libraries
Word2Vec
Twitter text
Speech recognition
TLSphinx
OpenEars
Computer vision
OpenCV
ccv
OpenFace
Tesseract
Low-level subroutine libraries
Eigen
fmincg-c
IntuneFeatures
SigmaSwiftStatistics
STEM
Swix
LibXtract
libLBFGS
NNPACK
Upsurge
YCMatrix
Choosing a deep learning framework
Summary
Optimizing Neural Networks for Mobile Devices
Delivering perfect user experience
Calculating the size of a convolutional neural network
Lossless compression
Compact CNN architectures
SqueezeNet
MobileNets
ShuffleNet
CondenseNet
Preventing a neural network from growing big
Lossy compression
Optimizing for inference
Network pruning
Weights quantization
Reducing precision
Other approaches
Facebook s approach in Caffe2
Knowledge distillation
Tools
An example of the network compression
Summary
Bibliography
Best Practices
Mobile machine learning project life cycle
Preparatory stage
Formulate the problem
Define the constraints
Research the existing approaches
Research the data
Make design choices
Prototype creation
Data preprocessing
Model training, evaluation, and selection
Field testing
Porting or deployment for a mobile platform
Production
Best practices
Benchmarking
Privacy and differential privacy
Debugging and visualization
Documentation
Machine learning gremlins
Data kobolds
Tough data
Biased data
Batch effects
Goblins of training
Product design ogres
Magical thinking
Cargo cult
Feedback loops
Uncanny valley effect
Recommended learning resources
Mathematical background
Machine learning
Computer vision
NLP
Summary

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Machine Learning with Swift