Mobile Artificial Intelligence Projects

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.

主要特點

• 在Android和iOS上建立實用的、真實世界的AI專案


• 實現識別手寫數字、情感分析等任務


• 探索機器學習、深度學習和移動視覺的核心功能

書籍描述

由於深度學習的突破，我們正在見證人工智慧的革命。《移動人工智慧專案》讓您能夠參與這場革命，將人工智慧（AI）技術應用於自然語言處理（NLP）、機器人和計算機視覺的應用設計。

本書教您如何在移動應用中利用AI的力量，同時學習NLP、神經網絡、深度學習和移動視覺的核心功能。書中涵蓋了一系列專案，包括房地產價格預測、識別手寫數字、預測車輛損壞和情感分析等任務。您將學習利用NLP和機器學習算法使應用更具預測性、主動性和能夠在較少人工輸入的情況下做出自主決策。在結尾章節中，您將在Android和iOS平台上使用流行的庫，如TensorFlow Lite、CoreML和PyTorch。

通過閱讀本書，您將開發出令人興奮且更直觀的移動應用，為用戶提供定制和更個性化的體驗。

您將學到什麼

• 探索人工智慧、深度學習和神經網絡的概念和基礎知識


• 實現機器視覺和自然語言處理的應用案例


• 使用TensorFlow建立預測車輛損壞的機器學習模型


• 在移動設備上部署TensorFlow以將語音轉換為文本


• 實現GAN來識別手寫數字


• 開發運用人工智慧原則的端到端移動應用


• 使用流行的庫，如TensorFlow Lite、CoreML和PyTorch

適合閱讀對象

《移動人工智慧專案》適合機器學習專業人士、深度學習工程師、人工智慧工程師和軟體工程師，他們希望將AI技術整合到基於移動平台和應用中。只需具備機器學習的基礎知識和任何一種程式語言的經驗，您就可以開始閱讀本書。

Arun Padmanabhan is a Machine Learning consultant with over 8 years of experience building end-to-end machine learning solutions and applications. Currently working with a couple of start-ups in the Financial and Insurance industries, he specializes in automating manual workflows using AI and creating Machine Vision and NLP applications. In past, he has led the data science team of a Singapore based product startup in the restaurant domain. He also has built stand-alone and integrated Machine Learning solutions in the Manufacturing, Shipping and e-commerce domains over the years. His interests are in research, development and applications of Artificial Intelligence and Deep Architectures.

Arun Padmanabhan 是一位擁有超過8年經驗的機器學習顧問，專注於建立端到端的機器學習解決方案和應用。目前他正在與金融和保險行業的幾家初創公司合作，專注於使用人工智能自動化手動工作流程，並創建機器視覺和自然語言處理應用。過去，他曾領導新加坡一家餐飲領域的產品初創公司的數據科學團隊。多年來，他還在製造、航運和電子商務領域建立了獨立和集成的機器學習解決方案。他的興趣在於人工智能和深度架構的研究、開發和應用。

1. Artificial Intelligence Concepts and Fundamentals
2. Creating a Real-Estate price prediction mobile app
3. Implementing Deepnet Architectures to Recognize Hand Written Digits
4. Building a Machine Vision Mobile App to Classify Flower Species
5. Building a 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's next?

- 人工智慧概念與基礎
- 建立一個房地產價格預測手機應用程式
- 實現深度神經網絡架構以識別手寫數字
- 建立一個機器視覺手機應用程式，用於分類花卉物種
- 使用 TensorFlow 建立一個預測汽車損壞的機器學習模型
- 在自然語言處理和循環神經網絡上進行 PyTorch 實驗
- 使用 WaveNet 模型在移動設備上進行語音轉文字的 TensorFlow 應用
- 實現生成對抗網絡以識別手寫數字
- 使用 LinearSVC 進行文本情感分析
- 接下來是什麼？