Acerca de este Curso

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Certificado para compartir
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100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio

Basic understanding of Kotlin and/or Swift

Aprox. 10 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Qué aprenderás

  • Prepare models for battery-operated devices

  • Execute models on Android and iOS platforms

  • Deploy models on embedded systems like Raspberry Pi and microcontrollers

Habilidades que obtendrás

TensorFlow LiteMathematical OptimizationMachine LearningTensorflowObject Detection
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio

Basic understanding of Kotlin and/or Swift

Aprox. 10 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Instructor

ofrecido por

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deeplearning.ai

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

6 horas para completar

Device-based models with TensorFlow Lite

6 horas para completar
14 videos (Total 40 minutos), 6 lecturas, 2 cuestionarios
14 videos
A few words from Laurence55s
Features and components of mobile AI2m
Architecture and performance3m
Optimization Techniques2m
Saving, converting, and optimizing a model3m
Examples2m
Quantization3m
TF-Select1m
Paths in Optimization1m
Running the models1m
Transfer learning3m
Converting a model to TFLite1m
Transfer learning with TFLite5m
6 lecturas
Prerequisites10m
Downloading the Coding Examples and Exercises10m
GPU delegates10m
Learn about supported ops and TF-Select10m
Week 1 Wrap up10m
Exercise Description10m
1 ejercicio de práctica
Week 1 Quiz
Semana
2

Semana 2

1 hora para completar

Running a TF model in an Android App

1 hora para completar
15 videos (Total 36 minutos), 3 lecturas, 1 cuestionario
15 videos
Installation and resources2m
Architecture of a model1m
Initializing the Interpreter2m
Preparing the Input1m
Inference and results1m
Code walkthrough3m
Run the App2m
Classifying camera images55s
Initialize and prepare input3m
Demo of camera image classifier4m
Initialize model and prepare inputs1m
Inference and results3m
Demo of the object detection App1m
Code for the inference and results2m
3 lecturas
Android fundamentals and installation10m
Week 2 Wrap up10m
Description10m
1 ejercicio de práctica
Week 2 Quiz
Semana
3

Semana 3

2 horas para completar

Building the TensorFLow model on IOS

2 horas para completar
22 videos (Total 45 minutos), 8 lecturas, 1 cuestionario
22 videos
A few words from Laurence1m
What is Swift?45s
TerserflowLiteSwift1m
Cats vs Dogs App1m
Taking the initial steps3m
Scaling the image2m
More steps in the process3m
Looking at the App in Xcode5m
What have we done so far and how do we continue?41s
Using the App50s
App architecture1m
Model details1m
Initial steps4m
Final steps1m
Looking at the code for the image classification App4m
Object classification intro30s
TFL detect App53s
App architecture55s
Initial steps58s
Final steps3m
Looking at the code for the object detection model3m
8 lecturas
Important links10m
Apple’s developer's site 10m
Apple's API10m
More details10m
Camera related functionalities10m
The Coco dataset10m
Week 3 Wrap up10m
Description10m
1 ejercicio de práctica
Week 3 Quiz
Semana
4

Semana 4

2 horas para completar

TensorFlow Lite on devices

2 horas para completar
13 videos (Total 29 minutos), 7 lecturas, 1 cuestionario
13 videos
A few words from Laurence3m
Devices3m
Starting to work on a Raspberry Pi1m
How do we start?2m
Image classification1m
The 4 step process2m
Object detection1m
Back to the 4 step process4m
Raspberry Pi demo2m
Microcontrollers2m
Closing words by Laurence28s
A conversation with Andrew Ng1m
7 lecturas
Edge TPU models10m
Options to choose from10m
Pre optimized mobileNet10m
Object detection model trained on the coco10m
Suggested links10m
Description10m
Wrap up10m
1 ejercicio de práctica
Week 4 Quiz

Revisiones

Principales revisiones sobre DEVICE-BASED MODELS WITH TENSORFLOW LITE

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Acerca de Programa especializado: TensorFlow: Data and Deployment

Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. This Specialization builds upon skills learned in the TensorFlow in Practice Specialization. We recommend learners complete that Specialization prior to enrolling in TensorFlow: Data and Deployment....
TensorFlow: Data and Deployment

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