Book Image

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By : Anubhav Singh, Rimjhim Bhadani
Book Image

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By: Anubhav Singh, Rimjhim Bhadani

Overview of this book

Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart artificial intelligence assistant, augmented reality, and more. With the help of eight projects, you will learn how to integrate deep learning processes into mobile platforms, iOS, and Android. This will help you to transform deep learning features into robust mobile apps efficiently. You’ll get hands-on experience of selecting the right deep learning architectures and optimizing mobile deep learning models while following an application oriented-approach to deep learning on native mobile apps. We will later cover various pre-trained and custom-built deep learning model-based APIs such as machine learning (ML) Kit through Firebase. Further on, the book will take you through examples of creating custom deep learning models with TensorFlow Lite. Each project will demonstrate how to integrate deep learning libraries into your mobile apps, right from preparing the model through to deployment. By the end of this book, you’ll have mastered the skills to build and deploy deep learning mobile applications on both iOS and Android.
Table of Contents (13 chapters)

Understanding anomaly detection for authentication

Anomaly detection is a much-studied branch of machine learning. The term is simplistic in its meaning. Basically, it is a collection of methods for detecting anomalies. Imagine a bag of apples. To identify and pick out the bad apples would be an act of anomaly detection.

Anomaly detection is performed in several ways:

  • By identifying data samples in the dataset that are very different from the rest of the samples by using minimum-maximum ranges of columns
  • By plotting the data as a line graph and identifying sudden spikes in the graph
  • By plotting the data around a Gaussian curve and marking the points lying on the extreme ends as outliers (anomalies)

Some of the commonly used methods are support vector machines, Bayesian networks, and k-nearest neighbors. We will focus on anomaly detection in relation to security in this section...