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
ofrecido por
Programa Especializado - TensorFlow: Data and Deploymentdeeplearning.aiAcerca de este Curso
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Certificado para compartir
Obtén un certificado al finalizar
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. 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.
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. 10 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
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 JT30 de mar. de 2020
One of the most useful and exciting courses I've ever done! Especially for the information available in the last (4th) week. Very interesting material and full of practical potential!
por SP16 de abr. de 2020
Quite good course. It gives an opportunity for individuals to utilize tensor flow in day to day devices which makes it more appealing. Thanks for developing this course.
por MM11 de ene. de 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 SR14 de abr. de 2020
It's a bit fast and definitely tightly packed. The objectives are clear though --how to build/debug/deploy on various modern devices (Android, iOS, RaspPi, etc)
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
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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