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Machine Learning for Mobile : Practical Guide to Building Intelligent Mobile Applications Powered by Machine Learning.
This book will help you build intelligent mobile applications for Android and iOS using machine learning. In the process, you will use popular machine learning toolkits such as TensorFlow Lite, Core ML, ML Kit and Fritz to build and deploy state-of-the-art machine learning models for mobile devices.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: 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 learningReinforcement 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
- Chapter 2: Supervised and Unsupervised Learning AlgorithmsIntroduction 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; Summary; References; Chapter 3: 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; Summary; Further reading; Chapter 4: TensorFlow Mobile in Android; An introduction to TensorFlow; TensorFlow Lite components; Model-file format; Interpreter; Ops/Kernel
- Interface to hardware accelerationThe 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; Creating an activity; Summary; Chapter 5: Regression Using Core ML in iOS; Introduction to regression; Linear regression; Dataset; Dataset naming; Understanding the basics of Core ML; Solving the problem using regression in Core ML; Technical requirements