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Mobile Artificial Intelligence Projects

You're reading from  Mobile Artificial Intelligence Projects

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789344073
Pages 312 pages
Edition 1st Edition
Languages
Authors (3):
Karthikeyan NG Karthikeyan NG
Profile icon Karthikeyan NG
Arun Padmanabhan Arun Padmanabhan
Profile icon Arun Padmanabhan
Matt Cole Matt Cole
Profile icon Matt Cole
View More author details

Table of Contents (12) Chapters

Preface 1. Artificial Intelligence Concepts and Fundamentals 2. Creating a Real-Estate Price Prediction Mobile App 3. Implementing Deep Net Architectures to Recognize Handwritten Digits 4. Building a Machine Vision Mobile App to Classify Flower Species 5. Building an ML Model to Predict Car Damage Using TensorFlow 6. PyTorch Experiments on NLP and RNN 7. TensorFlow on Mobile with Speech-to-Text with the WaveNet Model 8. Implementing GANs to Recognize Handwritten Digits 9. Sentiment Analysis over Text Using LinearSVC 10. What is Next? 11. Other Books You May Enjoy

Building your first ML model

With the knowledge that you have gained from this book, you can start to develop your own model that runs on a mobile phone. You will need to identify the problem statement first. There are many use cases where you will not need an ML model; we can't unnecessarily force ML into everything. Consequently, you need to follow a step-by-step approach before you build your own model:

  1. Identify the problem.
  2. Plan the effectiveness of your model; decide whether the data could be useful in predicting the output for future, similar cases. For example, collecting the purchase history for people of a similar age, gender, and location will be helpful in predicting a new customer's purchasing preferences. However, the data won't be helpful in predicting the height of a new customer, if that is the data that you are looking for.
  3. Develop a simple model...
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