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Device-based Models with TensorFlow Lite · LearnSpace
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Device-based Models with TensorFlow Lite

Курс от DeepLearning.AI
Средний≈ 10.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. This second course teaches you how to run your machine learning models in mobile applications. You’ll learn how to prepare models for a lower-powered, battery-operated devices, then execute models on both Android and iOS platforms. Finally, you’ll explore how to deploy on embedded systems using TensorFlow on Raspberry Pi and microcontrollers. This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Навыки, которые вы освоите

TensorflowModel DeploymentEmbedded SystemsModel OptimizationApple iOSAndroid DevelopmentModel TrainingSwift ProgrammingMachine LearningiOS DevelopmentMobile DevelopmentComputer Vision

Программа курса

4 модулей · 100 учебных материалов

01Device-based models with TensorFlow Lite25 материалов

Course Introduction

Introduction, A conversation with Andrew NgВидеоPrerequisitesЧтениеDownloading the Ungraded Labs and Programming AssignmentsЧтение

Machine Learning Models in Mobile and Embedded Systems

A few words from LaurenceВидео

Учитесь у экспертов

Laurence Moroney

Instructor

Device-based Models with TensorFlow Lite
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Обучение на Coursera

≈ 10.3 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Бенгальский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Features and components of mobile AIВидео
Architecture and performanceВидео
GPU delegatesЧтение
Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!Чтение
Optimization TechniquesВидео

Taking a look at the saved model format

Saving, converting, and optimizing a modelВидеоExamplesВидеоQuantizationВидеоTF-SelectВидеоLearn about supported ops and TF-SelectЧтениеPaths in OptimizationВидео

First primer on running models on mobile devices

Running the modelsВидеоTransfer learningВидеоConverting a model to TFLiteВидеоTransfer learning with TFLiteВидеоWeek 1 QuizЗаданиеWeek 1 Wrap upЧтение

Lecture Notes (Optional)

Lecture Notes Week 1Чтение

Graded Exercise - Train Your Own Model and Convert It to TFLite

Exercise DescriptionЧтениеExercise 1 - Train Your Own Model and Convert It to TFLiteЛабораторнаяExercise 1 - Train Your Own Model and Convert It to TFLiteПрограммирование
02Running a TF model in an Android App20 материалов

Introduction

Introduction, A conversation with AndrewВидеоInstallation and resourcesВидеоAndroid fundamentals and installationЧтениеArchitecture of a modelВидеоInitializing the InterpreterВидеоPreparing the InputВидеоInference and resultsВидео

Basic image classification

Code walkthroughВидеоRun the AppВидео

Classifying camera images

Classifying camera imagesВидеоInitialize and prepare inputВидео

Code walkthrough - camera image classifier

Demo of camera image classifierВидео

Object detection

Initialize model and prepare inputsВидеоInference and resultsВидео

Code walkthrough of an object detection app

Demo of the object detection AppВидеоCode for the inference and resultsВидеоWeek 2 QuizЗаданиеWeek 2 Wrap upЧтение

Lecture Notes (Optional)

Lecture Notes Week 2Чтение

Optional Exercise: Rock Paper Scissors for Android

DescriptionЧтение
03Building the TensorFLow model on IOS32 материалов

Introduction

Introduction, A conversation with Andrew NgВидеоA few words from LaurenceВидеоWhat is Swift?ВидеоTensorFlowLiteSwiftВидеоImportant linksЧтениеCats vs Dogs AppВидеоTaking the initial stepsВидеоApple’s developer's site ЧтениеScaling the imageВидеоApple's APIЧтениеMore steps in the processВидео

Next steps

Looking at the App in XcodeВидеоWhat have we done so far and how do we continue?ВидеоUsing the AppВидеоApp architectureВидеоModel detailsВидеоMore detailsЧтениеInitial steps

Classification and detection

Looking at the code for the image classification AppВидеоObject classification introВидеоTFL detect AppВидеоApp architectureВидеоThe Coco datasetЧтениеInitial stepsВидеоFinal steps

Lecture Notes (Optional)

Lecture Notes Week 3Чтение

Optional Exercise - Rock, Paper, Scissors on iOS

DescriptionЧтение
04TensorFlow Lite on devices23 материалов

Introduction

Introduction, A conversation with Andrew NgВидеоA few words from LaurenceВидеоDevicesВидеоEdge TPU modelsЧтение

Example: Raspberry Pi

Starting to work on a Raspberry PiВидеоHow do we start?ВидеоOptions to choose fromЧтениеImage classificationВидеоPre optimized mobileNetЧтениеThe 4 step processВидеоObject detectionВидеоObject detection model trained on the cocoЧтениеBack to the 4 step processВидео

Raspberry pi demo

Raspberry Pi demoВидео

Microcontrollers

MicrocontrollersВидеоSuggested linksЧтение[IMPORTANT] Reminder about end of access to Lab NotebooksЧтениеWeek 4 QuizЗаданиеClosing words by LaurenceВидео

Lecture Notes (Optional)

Lecture Notes Week 4Чтение

Optional Exercise - Rock Paper Scissors on Raspberry Pi

DescriptionЧтение

Course 2 Wrap up

Wrap upЧтениеA conversation with Andrew NgВидео
Видео
Camera related functionalitiesЧтение
Final stepsВидео
Видео
Looking at the code for the object detection modelВидео
Week 3 QuizЗадание
Week 3 Wrap upЧтение