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Machine Learning Projects for Mobile Applications: Build Android and iOS applications using TensorFlow Lite and Core ML
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Bring magic to your mobile apps using TensorFlow Lite and Core ML
Key Features
• Explore machine learning using classification, analytics, and detection tasks.
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• Work with image, text and video datasets to delve into real-world tasks
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• Build apps for Android and iOS using Caffe, Core ML and Tensorflow Lite
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Book Description
Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so.
The book starts with the basics of machine learning concepts for mobile applications and how to get well equipped for further tasks. You will start by developing an app to classify age and gender using Core ML and Tensorflow Lite. You will explore neural style transfer and get familiar with how deep CNNs work. We will also take a closer look at Google's ML Kit for the Firebase SDK for mobile applications. You will learn how to detect handwritten text on mobile. You will also learn how to create your own Snapchat filter by making use of facial attributes and OpenCV. You will learn how to train your own food classification model on your mobile; all of this will be done with the help of deep learning techniques. Lastly, you will build an image classifier on your mobile, compare its performance, and analyze the results on both mobile and cloud using TensorFlow Lite with an RCNN.
By the end of this book, you will not only have mastered the concepts of machine learning but also learned how to resolve problems faced while building powerful apps on mobiles using TensorFlow Lite, Caffe2, and Core ML.
What you will learn
• Demystify the machine learning landscape on mobile
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• Age and gender detection using TensorFlow Lite and Core ML
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• Use ML Kit for Firebase for in-text detection, face detection, and barcode scanning
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• Create a digit classifier using adversarial learning
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• Build a cross-platform application with face filters using OpenCV
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• Classify food using deep CNNs and TensorFlow Lite on iOS
Who this book is for
Machine Learning Projects for Mobile Applications is for you if you are a data scientist, machine learning expert, deep learning, or AI enthusiast who fancies mastering machine learning and deep learning implementation with practical examples using TensorFlow Lite and CoreML. Basic knowledge of Python programming language would be an added advantage.
© 2018 Packt Publishing (E-bog): 9781788998468
Release date
E-bog: 31. oktober 2018
Andre kan også lide...
- Data Analysis with Python: A Modern Approach David Taieb
- Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter: Build scalable real-world projects to implement end-to-end neural networks on Android and iOS Rimjhim Bhadani
- Python Deep Learning Cookbook: Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python Indra den Bakker
- Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python Richard Burton
- Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow Rajalingappaa Shanmugamani
- Hands-on ML Projects with OpenCV Mugesh S.
- Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data Nathan George
- Hands-On Neuroevolution with Python: Build high-performing artificial neural network architectures using neuroevolution-based algorithms Iaroslav Omelianenko
- Building Machine Learning Systems with Python: Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow, 3rd Edition Matthieu Brucher
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