With all the basic ideas that you have gained from this book, you can start building your own application with ML capabilities. Furthermore, with all of the new ways to interact with devices such as Amazon Alexa, Google Home, or the Facebook portal, you will find more use cases to build ML applications. Ultimately, we are moving toward a world with more connected devices, bringing the connections and communications closer to us, and leading us to create better experiences with ML.
Mobile Artificial Intelligence Projects
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Mobile Artificial Intelligence Projects
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Overview of this book
We’re witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning. Mobile Artificial Intelligence Projects empowers you to take part in this revolution by applying Artificial Intelligence (AI) techniques to design applications for natural language processing (NLP), robotics, and computer vision.
This book teaches you to harness the power of AI in mobile applications along with learning the core functions of NLP, neural networks, deep learning, and mobile vision. It features a range of projects, covering tasks such as real-estate price prediction, recognizing hand-written digits, predicting car damage, and sentiment analysis. You will learn to utilize NLP and machine learning algorithms to make applications more predictive, proactive, and capable of making autonomous decisions with less human input. In the concluding chapters, you will work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch across Android and iOS platforms.
By the end of this book, you will have developed exciting and more intuitive mobile applications that deliver a customized and more personalized experience to users.
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Current Title:
Mobile Artificial Intelligence Projects
Table of Contents (12 chapters)
Preface
Free Chapter
Artificial Intelligence Concepts and Fundamentals
Creating a Real-Estate Price Prediction Mobile App
Implementing Deep Net Architectures to Recognize Handwritten Digits
Building a Machine Vision Mobile App to Classify Flower Species
Building an ML Model to Predict Car Damage Using TensorFlow
PyTorch Experiments on NLP and RNN
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
Implementing GANs to Recognize Handwritten Digits
Sentiment Analysis over Text Using LinearSVC
What is Next?
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