This step involves standardizing the sentence to make sure it contains tokens that are from the standard language dictionary only and not anything else, such as hashtags, colloquial words, and so on. All these are removed in this step.
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Introduction to Machine Learning on Mobile
- Supervised and Unsupervised Learning Algorithms
- Random Forest on iOS
- TensorFlow Mobile in Android
- Regression Using Core ML in iOS
- The ML Kit SDK
- Spam Message Detection
- Fritz
- Introduction to Fritz
- 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
- Using the existing Core ML model in an iOS application using Fritz
- Using the existing TensorFlow for mobile model in an Android application using Fritz
- Summary
- Neural Networks on Mobile
- Mobile Application Using Google Vision
- The Future of ML on Mobile Applications
- Question and Answers
- FAQs
- Data science
- Machine learning framework 
- 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
- References 
- FAQs
- Other Books You May Enjoy
Standardization
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