更新时间：2021-07-02 14:20:48
coverpage
Title Page
Copyright and Credits
Machine Learning for Mobile
About Packt
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Contributors
About the authors
About the reviewer
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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
Get in touch
Reviews
Introduction to Machine Learning on Mobile
Definition of machine learning
When is it appropriate to go for machine learning systems?
The machine learning process
Defining the machine learning problem
Preparing the data
Building the model
Selecting the right machine learning algorithm
Training the machine learning model
Testing the model
Evaluation of the model
Making predictions/Deploying in the field
Types of learning
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
Challenges in machine learning
Why use machine learning on mobile devices?
Ways to implement machine learning in mobile applications
Utilizing machine learning service providers for a machine learning model
Ways to train the machine learning model
On a desktop (training in the cloud)
On a device
Ways to carry out the inference – making predictions
Inference on a server
Inference on a device
Popular mobile machine learning tools and SDKs
Skills needed to implement on-device machine learning
Summary
Supervised and Unsupervised Learning Algorithms
Introduction to supervised learning algorithms
Deep dive into supervised learning algorithms
Naive Bayes
Decision trees
Linear regression
Logistic regression
Support vector machines
Random forest
Introduction to unsupervised learning algorithms
Deep dive into unsupervised learning algorithms
Clustering algorithms
Clustering methods
Hierarchical agglomerative clustering methods
K-means clustering
Association rule learning algorithm
References
Random Forest on iOS
Introduction to algorithms
Decision tree
Advantages of the decision tree algorithm
Disadvantages of decision trees
Advantages of decision trees
Random forests
Solving the problem using random forest in Core ML
Dataset
Naming the dataset
Technical requirements
Creating the model file using scikit-learn
Converting the scikit model to the Core ML model
Creating an iOS mobile application using the Core ML model
Further reading
TensorFlow Mobile in Android
An introduction to TensorFlow
TensorFlow Lite components
Model-file format
Interpreter
Ops/Kernel
Interface to hardware acceleration
The architecture of a mobile machine learning application
Understanding the model concepts
Writing the mobile application using the TensorFlow model
Writing our first program
Creating and Saving the TF model
Freezing the graph
Optimizing the model file
Creating the Android app
Copying the TF Model
