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)
PyTorch Experiments on NLP and RNN
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
Implementing GANs to Recognize Handwritten Digits

Building a deeper neural network

In this section, we will use the concepts we learned about in this chapter to build a deeper neural network to classify handwritten digits:

  1. We will start with a new notebook and then load the required dependencies:
import numpy as np
np.random.seed(42) import keras from keras.datasets import mnist from keras.models import Sequential
from keras.layers import Dense from keras.layers import Dropout # new! from keras.layers.normalization
# new! import BatchNormalization # new! from keras import regularizers # new! from keras.optimizers import SGD
  1. We will now load and pre-process the data:
(X_train,y_train),(X_test,y_test)= mnist.load_data()
X_train= X_train.reshape(60000,784).
X_test= X_test.reshape(10000,784).astype('float32')
X_train/=255 X_test/=255 n_classes=10 y_train=keras.utils.to_categorical(y_train,n_classes...