Machine LearningTensorflowadvanced deploymentObject DetectionJavaScriptConvolutional Neural NetworkTensorFlow.jsTensorFlow LiteMathematical OptimizationTensorFlow Servingfederated learningTensorFlow Hub
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TensorFlow: Data and Deployment 专项课程deeplearning.ai您将学到的内容有
Run models in your browser using TensorFlow.js
Prepare and deploy models on mobile devices using TensorFlow Lite
Access, organize, and process training data more easily using TensorFlow Data Services
Explore four advanced deployment scenarios using TensorFlow Serving, TensorFlow Hub, and TensorBoard
您将获得的技能
关于此 专项课程
30,025 次近期查看
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.
应用的学习项目
In the TensorFlow: Data and Deployment Specialization, you will learn to apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more, implementing projects you can add to your portfolio and show in interviews.
可分享的证书
完成后获得证书
100% 在线课程
立即开始，按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
需要一些相关领域经验。需要一些相关经验。
完成时间大约为4 个月
建议 4 小时/周
英语（English）
字幕：英语（English）
可分享的证书
完成后获得证书
100% 在线课程
立即开始，按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
需要一些相关领域经验。需要一些相关经验。
完成时间大约为4 个月
建议 4 小时/周
英语（English）
字幕：英语（English）
此专项课程包含 4 门课程
Browser-based Models with TensorFlow.js
414 个评分
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.
Device-based Models with TensorFlow Lite
222 个评分
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.
Data Pipelines with TensorFlow Data Services
166 个评分
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.
Advanced Deployment Scenarios with TensorFlow
135 个评分
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.
提供方
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.
常见问题
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.
Can I just enroll in a single course?
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.
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.
Can I take the course for free?
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.
Is this course really 100% online? Do I need to attend any classes in person?
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.
Will I earn university credit for completing the Specialization?
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.
How long does it take to complete the Specialization?
We recommend taking 4 weeks of study, 4-5 hours per week, to finish each course in the Specialization. The Specialization includes 4 courses.
What background knowledge is necessary?
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.
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