课程信息
4.4
657 个评分
72 个审阅
专项课程
100% online

100% online

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

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中级

中级

完成时间(小时)

完成时间大约为9 小时

建议:1 week of study, 8-10 hours/week...
可选语言

英语(English)

字幕:英语(English), 法语(French), 巴西葡萄牙语, 德语(German), 西班牙语(Spanish), 日语...

您将获得的技能

Application Programming Interfaces (API)EstimatorMachine LearningTensorflowCloud Computing
专项课程
100% online

100% online

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
中级

中级

完成时间(小时)

完成时间大约为9 小时

建议:1 week of study, 8-10 hours/week...
可选语言

英语(English)

字幕:英语(English), 法语(French), 巴西葡萄牙语, 德语(German), 西班牙语(Spanish), 日语...

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 7 分钟

Introduction

The tool we will use to write machine learning programs is TensorFlow and so in this course, we will introduce you to TensorFlow. In the first course, you learned how to formulate business problems as machine learning problems and in the second course, you learned how machine works in practice and how to create datasets that you can use for machine learning. Now that you have the data in place, you are ready to get started writing machine learning programs....
Reading
2 个视频(共 7 分钟)
Video2 个视频
Intro to Qwiklabs5分钟
完成时间(小时)
完成时间为 3 小时

Core TensorFlow

We will introduce you to the core components of TensorFlow and you will get hands-on practice building machine learning programs. You will compare and write lazy evaluation and imperative programs, work with graphs, sessions, variables, as finally debug TensorFlow programs....
Reading
19 个视频(共 72 分钟), 4 个测验
Video19 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 programs分钟
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 programs分钟
Demo: Debugging Full Programs3分钟
Quiz3 个练习
What is TensorFlow?2分钟
Graphs and Sessions8分钟
Core TensorFlow20分钟
2
完成时间(小时)
完成时间为 4 小时

Estimator API

In this module we will walk you through the Estimator API....
Reading
18 个视频(共 67 分钟), 4 个测验
Video18 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 API分钟
Lab Solution: Estimator API10分钟
Train on large datasets with Dataset API8分钟
Lab Intro: Scaling up TensorFlow ingest using batching分钟
Lab Solution: Scaling up TensorFlow ingest using batching5分钟
Big jobs, Distributed training6分钟
Monitoring with TensorBoard3分钟
Demo: TensorBoard UI分钟
Serving Input Function5分钟
Recap: Estimator API1分钟
Lab Intro: Creating a distributed training TensorFlow model with Estimator API分钟
Lab Solution: Creating a distributed training TensorFlow model with Estimator API7分钟
Quiz1 个练习
Estimator API18分钟
3
完成时间(小时)
完成时间为 2 小时

Scaling TensorFlow models with CMLE

I’m here to talk about how you would go about taking your TensorFlow model and training it on GCP’s managed infrastructure for machine learning model training and deployed....
Reading
6 个视频(共 29 分钟), 2 个测验
Video6 个视频
Why Cloud Machine Learning Engine?6分钟
Train a Model2分钟
Monitoring and Deploying Training Jobs2分钟
Lab Intro: Scaling TensorFlow with Cloud Machine Learning Engine分钟
Lab Solution: Scaling TensorFlow with Cloud Machine Learning Engine16分钟
Quiz1 个练习
Cloud MLE10分钟
完成时间(小时)
完成时间为 2 分钟

Summary

Here we summarize the TensorFlow topics we covered so far in this course. We'll revisit core TensorFlow code, the Estimator API, and end with scaling your machine learning models with Cloud Machine Learning Engine....
Reading
1 个视频(共 2 分钟)
Video1 个视频
Summary2分钟
4.4

热门审阅

创建者 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!

创建者 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.

关于 Google Cloud

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

> 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 < *Look for details below for COMPLETION CHALLENGE, receive a GCP t-shirt!* 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. Complete any GCP specialization from now through November 30, 2018 for an opportunity to receive a GCP t-shirt (while supplies last). See forums for details....
Machine Learning with TensorFlow on Google Cloud Platform

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