課程信息
38,986 次近期查看

Learner Career Outcomes

33%

完成這些課程後已開始新的職業生涯

38%

通過此課程獲得實實在在的工作福利

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為10 小時

建議:2-3 weeks of study, 8-10 hours/week...

英語(English)

字幕:法語(French), 巴西葡萄牙語, 德語(German), 英語(English), 西班牙語(Spanish), 日語...

您將獲得的技能

Application Programming Interfaces (API)EstimatorMachine LearningTensorflowCloud Computing

Learner Career Outcomes

33%

完成這些課程後已開始新的職業生涯

38%

通過此課程獲得實實在在的工作福利

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為10 小時

建議:2-3 weeks of study, 8-10 hours/week...

英語(English)

字幕:法語(French), 巴西葡萄牙語, 德語(German), 英語(English), 西班牙語(Spanish), 日語...

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 7 分鐘

Introduction

2 個視頻 (總計 7 分鐘)
2 個視頻
Intro to Qwiklabs5分鐘
完成時間為 3 小時

Core TensorFlow

19 個視頻 (總計 72 分鐘), 4 個測驗
19 個視頻
What is TensorFlow2分鐘
Benefits of a Directed Graph5分鐘
TensorFlow API Hierarchy3分鐘
Lazy Evaluation4分鐘
Graph and Session4分鐘
Evaluating a Tensor2分鐘
Visualizing a graph2分鐘
Tensors6分鐘
Variables6分鐘
Lab Intro: Writing low-level TensorFlow programs16
Lab Solution8分鐘
Introduction5分鐘
Shape problems3分鐘
Fixing shape problems2分鐘
Data type problems1分鐘
Debugging full programs4分鐘
Intro: Debugging full programs15
Demo: Debugging Full Programs3分鐘
3 個練習
What is TensorFlow?2分鐘
Graphs and Sessions8分鐘
Core TensorFlow20分鐘
2
完成時間為 4 小時

Estimator API

18 個視頻 (總計 67 分鐘), 4 個測驗
18 個視頻
Estimator API3分鐘
Pre-made Estimators5分鐘
Demo: Housing Price Model1分鐘
Checkpointing1分鐘
Training on in-memory datasets2分鐘
Lab Intro: Estimator API39
Lab Solution: Estimator API10分鐘
Train on large datasets with Dataset API8分鐘
Lab Intro: Scaling up TensorFlow ingest using batching35
Lab Solution: Scaling up TensorFlow ingest using batching5分鐘
Big jobs, Distributed training6分鐘
Monitoring with TensorBoard3分鐘
Demo: TensorBoard UI28
Serving Input Function5分鐘
Recap: Estimator API1分鐘
Lab Intro: Creating a distributed training TensorFlow model with Estimator API51
Lab Solution: Creating a distributed training TensorFlow model with Estimator API7分鐘
1 個練習
Estimator API18分鐘
3
完成時間為 2 小時

Scaling TensorFlow models

6 個視頻 (總計 29 分鐘), 1 個閱讀材料, 2 個測驗
6 個視頻
Why Cloud AI Platform?6分鐘
Train a Model2分鐘
Monitoring and Deploying Training Jobs2分鐘
Lab Intro: Scaling TensorFlow with Cloud AI Platform50
Lab Solution: Scaling TensorFlow with Cloud AI Platform16分鐘
1 個閱讀材料
Cloud ML Engine is now Cloud AI Platform10分鐘
1 個練習
Cloud AI Platform10分鐘
完成時間為 2 分鐘

Summary

1 個視頻 (總計 2 分鐘)
1 個視頻
Summary2分鐘
4.5
193 條評論Chevron Right

來自Intro to TensorFlow的熱門評論

創建者 DWOct 17th 2018

pretty good. some of the code in the last lab could be better explained. also please debug the cloud shell, as it does not always show the "web preview" button ;) otherwise, good job!

創建者 SSJun 6th 2018

Nice introduce, might be more on introduce the model structure, because I still need to read additional notes to locate how to train my deep learning model online.

關於 Google 云端平台

We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success....

關於 Machine Learning with TensorFlow on Google Cloud Platform 專項課程

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

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