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.
Este curso forma parte de Programa Especializado - TensorFlow: Data and Deployment
Device-based Models with TensorFlow Lite
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Acerca de este Curso
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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
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 2 de 4 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Basic understanding of Kotlin and/or Swift
Aprox. 11 horas para completar
Sugerido: 4 weeks of study, 4-5 hours/week
Inglés (English)
Subtítulos: Inglés (English)
Programa - Qué aprenderás en este curso
6 horas para completar
Device-based models with TensorFlow Lite
Welcome to this course on TensorFlow Lite, an exciting technology that allows you to put your models directly and literally into people's hands. You'll start with a deep dive into the technology, and how it works, learning about how you can optimize your models for mobile use -- where battery power and processing power become an important factor. You'll then look at building applications on Android and iOS that use models, and you'll see how to use the TensorFlow Lite Interpreter in these environments. You'll wrap up the course with a look at embedded systems and microcontrollers, running your models on Raspberry Pi and SparkFun Edge boards.
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
1 hora para completar
Running a TF model in an Android App
Last week you learned about TensorFlow Lite and you saw how to convert your models from TensorFlow to TensorFlow Lite format. You also learned about the standalone TensorFlow Lite Interpreter which could be used to test these models. You wrapped with an exercise that converted a Fashion MNIST based model to TensorFlow Lite and then tested it with the interpreter.
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
2 horas para completar
Building the TensorFLow model on IOS
The other popular mobile operating system is, of course, iOS. So this week you'll do very similar tasks to last week -- learning how to take models and run them on iOS. You'll need some programming background with Swift for iOS to fully understand everything we go through, but even if you don't have this expertise, I think this weeks content is something you'll find fun to explore -- and you'll learn how to build a variety of ML applications that run on this important operating system!
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
2 horas para completar
TensorFlow Lite on devices
Now that you've looked at TensorFlow Lite and explored building apps on Android and iOS that use it, the next and final step is to explore embedded systems like Raspberry Pi, and learn how to get your models running on that. The nice thing is that the Pi is a full Linux system, so it can run Python, allowing you to either use the full TensorFlow for Training and Inference, or just the Interpreter for Inference. I'd recommend the latter, as training on a Pi can be slow!
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
por MMJan 11, 2020
Great course, very practical in the real world. It also balances and accommodates developers on what devices you have available. Looking forward to the next course
por RSFeb 14, 2020
Just one recommendation, may be an exercise on a NLP Model deployment (Text or audio) could have been added rather than all 3 examples of computer vision
por FLMar 18, 2020
Same as the previous course of this specialization:\n\nThe assignments are not very challenging. But the exercises are really cool!!
por SMFeb 4, 2020
excellent course with practical examples on using TensorFlow Lite on Raspberry, Android and iOS
por BBMar 22, 2020
Nice Course! The course contents are awesome eager to use this in all my learning in future
por MRJan 4, 2020
A great course to learn how to implement any Deep Learning models on edge devices.
por CFFeb 8, 2020
Great course, a bit short of content and exercises but it was well designed.
por IMMar 10, 2020
It could be more coding exercises, but as for the general info it's great.
por QDFeb 9, 2020
great!!!exactly what i want for my undergrad thesis application
por JRFeb 25, 2020
good demo for IOS and Android apps use tensorflow lite
ofrecido por
deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
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.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
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