Book Image

Mobile Artificial Intelligence Projects

By : Karthikeyan NG, Arun Padmanabhan, Matt R. Cole
Book Image

Mobile Artificial Intelligence Projects

By: Karthikeyan NG, Arun Padmanabhan, Matt R. Cole

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.
Table of Contents (12 chapters)
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

Datasets for image classification

For our flower classification example, we will be using the University of Oxford's Visual Geometry Group (VGG) image dataset collection. The collection can be accessed at http://www.robots.ox.ac.uk/~vgg/data/.

The VGG is the same department that won previous ImageNet competitions. The pretrained models, such as VGG14 and VGG16, were built by this department and they won in 2014 and 2016, respectively. These datasets are used by the VGG to train and evaluate the models that they build.

The flower dataset can be found in the Fine-Grain Recognition Datasets section of the page, along with textures and pet datasets. Click on Flower Category Datasets, or use the following link to access the flower datasets from VGG, http://www.robots.ox.ac.uk/~vgg/data/flowers/.

Here, you can find two datasets, one with 17 different species of flowers, and the...