What this book covers

Chapter 1, Mobile Landscapes in Machine Learning, makes us familiar with the basic ideas behind TensorFlow Lite and Core ML.

Chapter 2, CNN Based Age and Gender Identification Using Core ML, teaches us how to build an iOS application to detect the age, gender, and emotion of a person from a camera feed or from the user's photo gallery using the existing data models that were built for the same purpose.

Chapter 3, Applying Neural Style Transfer on Photos, teaches us how to build a complete iOS and Android application in which image transformations are applied to our own images in a fashion similar to the Instagram app.

Chapter 4, Deep Diving into the ML Kit with Firebase, explores the Google Firebase-based ML Kit platform for mobile applications.

Chapter 5, A Snapchat-Like AR Filter on Android, takes us on a journey where we will build an AR filter that is used on applications such as Snapchat and Instagram using TensorFlow Lite.

Chapter 6, Handwritten Digit Classifier Using Adversarial Learning, explains how to build an Android application that identifies handwritten digits. 

Chapter 7, Face-Swapping with Your Friends Using OpenCV, takes a close look at building an application where a face in an image is replaced by another face.

Chapter 8, Classifying Food Using Transfer Learning, explains how to classify food items using transfer learning. 

Chapter 9, What's Next?, gives us a glimpse into all the applications built throughout the book and their relevance in the future.