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Mobile Artificial Intelligence Projects电子书

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30人正在读 | 0人评论 6.5

作       者:Karthikeyan NG

出  版  社:Packt Publishing

出版时间:2019-03-30

字       数:28.4万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Learn to build end-to-end AI apps from scratch for Android and iOS using TensorFlow Lite, CoreML, and PyTorch Key Features * Build practical, real-world AI projects on Android and iOS * Implement tasks such as recognizing handwritten digits, sentiment analysis, and more * Explore the core functions of machine learning, deep learning, and mobile vision Book Description 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. What you will learn * Explore the concepts and fundamentals of AI, deep learning, and neural networks * Implement use cases for machine vision and natural language processing * Build an ML model to predict car damage using TensorFlow * Deploy TensorFlow on mobile to convert speech to text * Implement GAN to recognize hand-written digits * Develop end-to-end mobile applications that use AI principles * Work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch Who this book is for Mobile Artificial Intelligence Projects is for machine learning professionals, deep learning engineers, AI engineers, and software engineers who want to integrate AI technology into mobile-based platforms and applications. Sound knowledge of machine learning and experience with any programming language is all you need to get started with this book.
目录展开

Dedication

About Packt

Why subscribe?

Packt.com

Contributors

About the authors

About the reviewers

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Artificial Intelligence Concepts and Fundamentals

AI versus machine learning versus deep learning

Evolution of AI

The mechanics behind ANNs

Biological neurons

Working of artificial neurons

Scenario 1

Scenario 2

Scenario 3

ANNs

Activation functions

Sigmoid function

Tanh function

ReLU function

Cost functions

Mean squared error

Cross entropy

Gradient descent

Backpropagation – a method for neural networks to learn

Softmax

TensorFlow Playground

Summary

Further reading

Creating a Real-Estate Price Prediction Mobile App

Setting up the artificial intelligence environment

Downloading and installing Anaconda

Advantages of Anaconda

Creating an Anaconda environment

Installing dependencies

Building an ANN model for prediction using Keras and TensorFlow

Serving the model as an API

Building a simple API to add two numbers

Building an API to predict the real estate price using the saved model

Creating an Android app to predict house prices

Downloading and installing Android Studio

Creating a new Android project with a single screen

Designing the layout of the screen

Adding a functionality to accept input

Adding a functionality to consume the RESTful API that serves the model

Additional notes

Creating an iOS app to predict house prices

Downloading and installing Xcode

Creating a new iOS project with a single screen

Designing the layout of the screen

Adding a functionality to accept input

Adding a functionality to consume the RESTful API that serves the model

Additional notes

Summary

Implementing Deep Net Architectures to Recognize Handwritten Digits

Building a feedforward neural network to recognize handwritten digits, version one

Building a feedforward neural network to recognize handwritten digits, version two

Building a deeper neural network

Introduction to Computer Vision

Machine learning for Computer Vision

Conferences help on Computer Vision

Summary

Further reading

Building a Machine Vision Mobile App to Classify Flower Species

CoreML versus TensorFlow Lite

CoreML

TensorFlow Lite

What is MobileNet?

Datasets for image classification

Creating your own image dataset using Google images

Alternate approach of creating custom datasets from videos

Building your model using TensorFlow

Running TensorBoard

Summary

Building an ML Model to Predict Car Damage Using TensorFlow

Transfer learning basics

Approaches to transfer learning

Building the TensorFlow model

Installing TensorFlow

Training the images

Building our own model

Retraining with our own images

Architecture

Distortions

Hyperparameters

Image dataset collection

Introduction to Beautiful Soup

Examples

Dataset preparation

Running the training script

Setting up a web application

Summary

PyTorch Experiments on NLP and RNN

PyTorch

The features of PyTorch

Installing PyTorch

PyTorch basics

Using variables in PyTorch

Plotting values on a graph

Building our own model network

Linear regression

Classification

Simple neural networks with torch

Saving and reloading data on the network

Running with batches

Optimization algorithms

Recurrent neural networks

The MNIST database

RNN classification

RNN cyclic neural network – regression

Natural language processing

Affine maps

Non-linearities

Objective functions

Building network components in PyTorch

BoW classifier using logistic regression

Summary

TensorFlow on Mobile with Speech-to-Text with the WaveNet Model

WaveNet

Architecture

Network layers in WaveNet

The algorithm's components

Building the model

Dependencies

Datasets

Preprocessing the dataset

Training the network

Testing the network

Transforming a speech WAV file into English text

Getting the model

Bazel build TensorFlow and quantizing the model

TensorFlow ops registration

Building an Android application

Requirements

Summary

Implementing GANs to Recognize Handwritten Digits

Introduction to GANs

Generative versus discriminative algorithms

How GANs work

Understanding the MNIST database

Building the TensorFlow model

Training the neural network

Building the Android application

Digit classifier

Summary

Sentiment Analysis over Text Using LinearSVC

Building the ML model using scikit–learn

Scikit-learn

The scikit-learn pipeline

LinearSVC

Building the iOS application

Summary

What is Next?

Popular ML–based cloud services

IBM Watson services

Microsoft Azure Cognitive Services

Vision APIs

Speech APIs

Knowledge APIs

Search APIs

Language APIs

Amazon ML

Vision services

Chat services

Language services

Google Cloud ML

Building your first ML model

The limitations of building your own model

Personalized user experience

Providing better search results

Targeting the right user

Summary

Further reading

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