この専門講座について

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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.

共有できる証明書
修了時に証明書を取得
100%オンラインコース
自分のスケジュールですぐに学習を始めてください。
フレキシブルなスケジュール
柔軟性のある期限の設定および維持
中級レベル
約4か月で修了
推奨4時間/週
英語
字幕:英語
共有できる証明書
修了時に証明書を取得
100%オンラインコース
自分のスケジュールですぐに学習を始めてください。
フレキシブルなスケジュール
柔軟性のある期限の設定および維持
中級レベル
約4か月で修了
推奨4時間/週
英語
字幕:英語

この専門講座には4コースあります。

コース1

コース 1

Browser-based Models with TensorFlow.js

4.7
408件の評価
102件のレビュー
コース2

コース 2

Device-based Models with TensorFlow Lite

4.6
220件の評価
51件のレビュー
コース3

コース 3

Data Pipelines with TensorFlow Data Services

4.0
160件の評価
53件のレビュー
コース4

コース 4

Advanced Deployment Scenarios with TensorFlow

4.6
131件の評価
26件のレビュー

提供:

deeplearning.ai ロゴ

deeplearning.ai

よくある質問

  • 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! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • 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.

  • 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. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • We recommend taking 4 weeks of study, 4-5 hours per week, to finish each course in the Specialization. The Specialization includes 4 courses.

  • We suggest taking the TensorFlow in Practice Specialization first to develop basic familiarity with modeling in TensorFlow. You should also be comfortable using JavaScript and Swift, which you'll use in Courses 1 and 2. If you want to get a deeper, foundational understanding of how neural networks work, you can take the Deep Learning Specialization.

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。