课程信息

82,599 次近期查看

学生职业成果

33%

完成这些课程后已开始新的职业生涯

38%

通过此课程获得实实在在的工作福利
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为11 小时
英语(English)
字幕:法语(French), 巴西葡萄牙语, 德语(German), 英语(English), 西班牙语(Spanish), 日语...

您将获得的技能

Application Programming Interfaces (API)EstimatorMachine LearningTensorflowCloud Computing

学生职业成果

33%

完成这些课程后已开始新的职业生涯

38%

通过此课程获得实实在在的工作福利
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为11 小时
英语(English)
字幕:法语(French), 巴西葡萄牙语, 德语(German), 英语(English), 西班牙语(Spanish), 日语...

讲师

提供方

Google 云端平台 徽标

Google 云端平台

教学大纲 - 您将从这门课程中学到什么

内容评分Thumbs Up90%(2,594 个评分)Info
1

1

完成时间为 7 分钟

Introduction

完成时间为 7 分钟
2 个视频 (总计 7 分钟)
2 个视频
Intro to Qwiklabs5分钟
完成时间为 4 小时

Core TensorFlow

完成时间为 4 小时
19 个视频 (总计 72 分钟)
19 个视频
What is TensorFlow2分钟
Benefits of a Directed Graph5分钟
TensorFlow API Hierarchy3分钟
Lazy Evaluation4分钟
Graph and Session4分钟
Evaluating a Tensor2分钟
Visualizing a graph2分钟
Tensors6分钟
Variables6分钟
Lab Intro: Writing low-level TensorFlow programs16
Lab Solution8分钟
Introduction5分钟
Shape problems3分钟
Fixing shape problems2分钟
Data type problems1分钟
Debugging full programs4分钟
Intro: Debugging full programs15
Demo: Debugging Full Programs3分钟
3 个练习
What is TensorFlow?30分钟
Graphs and Sessions30分钟
Core TensorFlow30分钟
2

2

完成时间为 5 小时

Estimator API

完成时间为 5 小时
18 个视频 (总计 67 分钟)
18 个视频
Estimator API3分钟
Pre-made Estimators5分钟
Demo: Housing Price Model1分钟
Checkpointing1分钟
Training on in-memory datasets2分钟
Lab Intro: Estimator API39
Lab Solution: Estimator API10分钟
Train on large datasets with Dataset API8分钟
Lab Intro: Scaling up TensorFlow ingest using batching35
Lab Solution: Scaling up TensorFlow ingest using batching5分钟
Big jobs, Distributed training6分钟
Monitoring with TensorBoard3分钟
Demo: TensorBoard UI28
Serving Input Function5分钟
Recap: Estimator API1分钟
Lab Intro: Creating a distributed training TensorFlow model with Estimator API51
Lab Solution: Creating a distributed training TensorFlow model with Estimator API7分钟
1 个练习
Estimator API30分钟
3

3

完成时间为 2 小时

Scaling TensorFlow models

完成时间为 2 小时
6 个视频 (总计 29 分钟), 1 个阅读材料, 2 个测验
6 个视频
Why Cloud AI Platform?6分钟
Train a Model2分钟
Monitoring and Deploying Training Jobs2分钟
Lab Intro: Scaling TensorFlow with Cloud AI Platform50
Lab Solution: Scaling TensorFlow with Cloud AI Platform16分钟
1 个阅读材料
Cloud ML Engine is now Cloud AI Platform10分钟
1 个练习
Cloud AI Platform30分钟
完成时间为 2 分钟

Summary

完成时间为 2 分钟
1 个视频 (总计 2 分钟)
1 个视频
Summary2分钟

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关于 Machine Learning with TensorFlow on Google Cloud Platform 专项课程

What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform. > By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <...
Machine Learning with TensorFlow on Google Cloud Platform

常见问题

  • Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

  • If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

  • Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

  • If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

  • This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.

  • 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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