Об этом курсе

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Курс 4 из 4 в программе
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Промежуточный уровень

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.

Прибл. 12 часов на выполнение
Английский
Субтитры: Английский

Чему вы научитесь

  • Use TensorFlow Serving to do inference over the web

  • Navigate TensorFlow Hub, a repository of models that you can use for transfer learning

  • Evaluate how your models work and share model metadata using TensorBoard

  • Explore federated learning and how to retrain deployed models while maintaining data privacy

Приобретаемые навыки

TensorFlow ServingMachine Learningfederated learningTensorFlow HubTensorBoard
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 4 из 4 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Промежуточный уровень

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.

Прибл. 12 часов на выполнение
Английский
Субтитры: Английский

Преподаватели

от партнера

Логотип deeplearning.ai

deeplearning.ai

Программа курса: что вы изучите

Неделя
1

Неделя 1

1 ч. на завершение

TensorFlow Extended

1 ч. на завершение
12 видео ((всего 21 мин.)), 5 материалов для самостоятельного изучения, 1 тест
12 видео
Introduction24
Serving3мин
Installing TF Serving1мин
TensorFlow Serving summary30
Setup for serving2мин
Serving1мин
Predictions41
Passing data to serving1мин
Getting the predictions back1мин
Running the colab2мин
Complex model2мин
5 материалов для самостоятельного изучения
Downloading the Coding Examples and Exercises10мин
Installation link10мин
TF server running in colab10мин
Serving with Fashion MNIST10мин
Ungraded Exercise - Serving with MNIST10мин
1 практическое упражнение
Week 1 Quiz
Неделя
2

Неделя 2

5 ч. на завершение

Sharing pre-trained models with TensorFlow Hub

5 ч. на завершение
11 видео ((всего 20 мин.)), 7 материалов для самостоятельного изучения, 2 тестов
11 видео
Introduction to TF Hub2мин
Transfer learning1мин
Inference1мин
Module storage2мин
Text based models1мин
Word embeddings1мин
Experimenting with embeddings1мин
Colab1мин
Classify cats and dogs1мин
Transfer learning1мин
7 материалов для самостоятельного изучения
Tensorflow Hub link10мин
Link to saved models10мин
Colab10мин
Pre-trained Word Embeddings10мин
Text Classification Colab10мин
MobileNet model details10мин
Colab10мин
1 практическое упражнение
Week 2 Quiz
Неделя
3

Неделя 3

5 ч. на завершение

Tensorboard: tools for model training

5 ч. на завершение
10 видео ((всего 16 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
10 видео
Tensorboard scalars1мин
Callbacks42
Histograms59
Publishing model details1мин
Local tensorboard2мин
Looking at graphics in a dataset2мин
More than one image56
Confusion matrix2мин
Multiple callbacks1мин
2 материала для самостоятельного изучения
tensorboard.dev10мин
Colab10мин
1 практическое упражнение
Week 3 Quiz4мин
Неделя
4

Неделя 4

1 ч. на завершение

Federated Learning

1 ч. на завершение
9 видео ((всего 22 мин.)), 1 материал для самостоятельного изучения, 1 тест
9 видео
Training on mobile devices2мин
Data at the edge2мин
How it works2мин
Maintaining user privacy3мин
Masking2мин
APIs for Federated Learning2мин
Example of federated learning2мин
Outro59
1 материал для самостоятельного изучения
Colab10мин
1 практическое упражнение
Week 4 Quiz30мин

Рецензии

Лучшие отзывы о курсе ADVANCED DEPLOYMENT SCENARIOS WITH TENSORFLOW

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Специализация TensorFlow: Data and Deployment: общие сведения

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

Часто задаваемые вопросы

  • 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.

  • 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.

  • 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.

  • 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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