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Gopalakrishnan, Revathi. Machine learning for mobile: practical guide to building intelligent mobile applications powered by machine learning / Revathi Gopalakrishnan, Avinash Venkateswarlu. — 1 online resource (1 volume) : illustrations — <URL:http://elib.fa.ru/ebsco/1993349.pdf>.

Record create date: 2/25/2019

Subject: Application software — Development.; Mobile apps.; Machine learning.; Mobile computing.; COMPUTERS / Software Development & Engineering / General

Collections: EBSCO

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Table of Contents

  • 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 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
  • Chapter 2: 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
    • 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 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
        • 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
      • How to create the model file using scikit-learn
      • Running and testing the model
      • Importing the model into the iOS project
      • Writing the iOS application
      • Running the iOS application
    • Further reading
    • Summary
  • Chapter 6: The ML Kit SDK
    • Understanding ML Kit
      • ML Kit APIs
        • Text recognition
        • Face detection
        • Barcode scanning
        • Image labeling
        • Landmark recognition
        • Custom model inference
    • Creating a text recognition app using Firebase on-device APIs
    • Creating a text recognition app using Firebase on-cloud APIs
    • Face detection using ML Kit
      • Face detection concepts
      • Sample solution for face detection using ML Kit
      • Running the app
    • Summary
  • Chapter 7: Spam Message Detection
    • Understanding NLP
      • Introducing NLP
      • Text-preprocessing techniques
        • Removing noise
        • Normalization
        • Standardization
      • Feature engineering
        • Entity extraction
        • Topic modeling
        • Bag-of-words model
        • Statistical Engineering
        • TF–IDF
        • TF
        • Inverse Document Frequency (IDF)
        • TF-IDF
      • Classifying/clustering the text
    • Understanding linear SVM algorithm
    • Solving the problem using linear SVM in Core ML
      • About the data
      • Technical requirements
      • Creating the Model file using Scikit Learn 
      • Converting the scikit-learn model into the Core ML model
      • Writing the iOS application
    • Summary
  • Chapter 8: Fritz
    • Introduction to Fritz
      • Prebuilt ML models
      • Ability to use custom models
      • Model management
    • Hand-on samples using Fritz
      • Using the existing TensorFlow for mobile model in an Android application using Fritz
        • Registering with Fritz
        • Uploading the model file (.pb or .tflite)
        • Setting up Android and registering the app
        • Adding Fritz's TFMobile library
        • Adding dependencies to the project
        • Registering the FritzJob service in your Android Manifest
        • Replacing the TensorFlowInferenceInterface class with Fritz Interpreter
        • Building and running the application
        • Deploying a new version of your model
      • Creating an android application using fritz pre-built models
        • Adding dependencies to the project
        • Registering the Fritz JobService in your Android Manifest
        • Creating the app layout and components
        • Coding the application
      • Using the existing Core ML model in an iOS application using Fritz
        • Registering with Fritz
        • Creating a new project in Fritz
        • Uploading the model file (.pb or .tflite)
        • Creating an Xcode project
        • Installing Fritz dependencies
        • Adding code
        • Building and running the iOS mobile application
    • Summary
  • Chapter 9: Neural Networks on Mobile
    • Introduction to neural networks
      • Communication steps of  a neuron
      • The activation function
      • Arrangement of neurons
      • Types of neural networks
    • Image recognition solution
    • Creating a TensorFlow image recognition model
      • What does TensorFlow do?
      • Retraining the model
        • About bottlenecks
      • Converting the TensorFlow model into the Core ML model
      • Writing the iOS mobile application
    • Handwritten digit recognition solution
    • Introduction to Keras
    • Installing Keras
    • Solving the problem
      • Defining the problem statement
      • Problem solution
        • Preparing the data
        • Defining the model's architecture
        • Compiling and fitting the model
        • Converting the Keras model into the Core ML model
        • Creating the iOS mobile application
    • Summary
  • Chapter 10: Mobile Application Using Google Vision
    • Features of Google Cloud Vision
    • Sample mobile application using Google Cloud Vision
      • How does label detection work?
      • Prerequisites
      • Preparations
      • Understanding the Application
      • Output
    • Summary
  • Chapter 11: The Future of ML on Mobile Applications
    • Key ML mobile applications 
      • Facebook
      • Google Maps
      • Snapchat
      • Tinder
      • Netflix
      • Oval Money
      • ImprompDo
      • Dango
      • Carat
      • Uber
      • GBoard
    • Key innovation areas
      • Personalization applications
      • Healthcare
      • Targeted promotions and marketing
      • Visual and audio recognition
      • E-commerce 
      • Finance management
      • Gaming and entertainment
      • Enterprise apps
      • Real estate
      • Agriculture
      • Energy
      • Mobile security
    • Opportunities for stakeholders
      • Hardware manufacturers
      • Mobile operating system vendors
      • Third-party mobile ML SDK providers
      • ML mobile application developers
    • Summary
  • Question and Answers
    • FAQs
      • Data science
        • What is data science?
        • Where is data science used?
        • What is big data?
        • What is data mining?
        • Relationship between data science and big data
        • What are artificial neural networks?
        • What is AI?
        • How are data science, AI, and machine learning interrelated?
      • Machine learning framework 
        • Caffe2
        • scikit-learn
        • TensorFlow
        • Core ML
      • Mobile machine learning project implementation
        • What are the high-level important items to be considered before starting the project?
        • What are the roles and skills required to implement a mobile machine learning project?
        •  What should you focus on when testing the mobile machine learning project?
        • What is the help that the domain expert will provide to the machine learning project?
        • What are the common pitfalls in machine learning projects?
      • Installation
        • Python
        • Python dependencies
        • Xcode
    • References 
  • Other Books You May Enjoy
  • Index

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