Machine Learning with Swift by Sosnovshchenko, Oleksandr -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Machine Learning with Swift

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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

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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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