课程信息

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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26 小时

英语(English)

字幕:英语(English)

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26 小时

英语(English)

字幕:英语(English)

提供方

伦敦帝国学院 徽标

伦敦帝国学院

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 3 小时

Introduction to TensorFlow

完成时间为 3 小时
14 个视频 (总计 59 分钟), 8 个阅读材料
14 个视频
Welcome to week 11分钟
Hello TensorFlow!1分钟
[Coding tutorial] Hello TensorFlow!2分钟
What's new in TensorFlow 24分钟
Interview with Laurence Moroney5分钟
Introduction to Google Colab2分钟
[Coding tutorial] Introduction to Google Colab8分钟
TensorFlow documentation3分钟
TensorFlow installation3分钟
[Coding tutorial] pip installation3分钟
[Coding tutorial] Running TensorFlow with Docker10分钟
Upgrading from TensorFlow 13分钟
[Coding tutorial] Upgrading from TensorFlow 16分钟
8 个阅读材料
About Imperial College & the team10分钟
How to be successful in this course10分钟
Grading policy10分钟
Additional readings & helpful references10分钟
What is TensorFlow?10分钟
Google Colab resources10分钟
TensorFlow documentation10分钟
Upgrade TensorFlow 1.x Notebooks10分钟
2

2

完成时间为 7 小时

The Sequential model API

完成时间为 7 小时
13 个视频 (总计 59 分钟)
13 个视频
What is Keras?1分钟
Building a Sequential model4分钟
[Coding tutorial] Building a Sequential model4分钟
Convolutional and pooling layers4分钟
[Coding tutorial] Convolutional and pooling layers5分钟
The compile method5分钟
[Coding tutorial] The compile method5分钟
The fit method4分钟
[Coding tutorial] The fit method7分钟
The evaluate and predict methods6分钟
[Coding tutorial] The evaluate and predict methods4分钟
Wrap up and introduction to the programming assignment1分钟
2 个练习
[Knowledge check] Feedforward and convolutional neural networks15分钟
[Knowledge check] Optimisers, loss functions and metrics15分钟
3

3

完成时间为 7 小时

Validation, regularisation and callbacks

完成时间为 7 小时
11 个视频 (总计 60 分钟)
11 个视频
Interview with Andrew Ng6分钟
Validation sets4分钟
[Coding Tutorial] Validation sets9分钟
Model regularisation6分钟
[Coding Tutorial] Model regularisation4分钟
Introduction to callbacks5分钟
[Coding tutorial] Introduction to callbacks7分钟
Early stopping and patience6分钟
[Coding tutorial] Early stopping and patience5分钟
Wrap up and introduction to the programming assignment51
1 个练习
[Knowledge check] Validation and regularisation15分钟
4

4

完成时间为 7 小时

Saving and loading models

完成时间为 7 小时
12 个视频 (总计 74 分钟)
12 个视频
Saving and loading model weights6分钟
[Coding tutorial] Saving and loading model weights10分钟
Model saving criteria4分钟
[Coding tutorial] Model saving criteria11分钟
Saving the entire model4分钟
[Coding tutorial] Saving the entire model8分钟
Loading pre-trained Keras models5分钟
[Coding tutorial] Loading pre-trained Keras models7分钟
TensorFlow Hub modules2分钟
[Coding tutorial] TensorFlow Hub modules8分钟
Wrap up and introduction to the programming assignment1分钟

常见问题

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  • 您购买证书后,将有权访问所有课程材料,包括评分作业。完成课程后,您的电子课程证书将添加到您的成就页中,您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容,可以免费旁听课程。

  • 您可在付款后两周内,或者在课程第一个班次开课后(对于已启动的课程)两周内,获得全额退款,以其中较晚者为准。获得课程证书后,您便无法再退款;即使您在两周的退款期内完成了课程,也是如此。请阅读我们完整的退款政策

  • 是的,Coursera 可以向无法承担学费的学生提供助学金。点击左侧‘注册’按钮下的‘助学金’链接即可申请助学金。您可以根据屏幕提示完成申请,申请获批后会收到通知。了解详情

  • Jupyter Notebooks are a third-party tool that some Coursera courses use for programming assignments.

    You can revert your code or get a fresh copy of your Jupyter Notebook mid-assignment. By default, Coursera persistently stores your work within each notebook.

    To keep your old work and also get a fresh copy of the initial Jupyter Notebook, click File, then Make a copy.

    We recommend keeping a naming convention such as “Assignment 1 - Initial” or “Copy” to keep your notebook environment organized. You can also download this file locally.

    Refresh your notebook

    1. Rename your existing Jupyter Notebook within the individual notebook view
    2. In the notebook view, add “?forceRefresh=true” to the end of your notebook URL
    3. Reload the screen
    4. You will be directed to your home Learner Workspace where you’ll see both old and new Notebook files.
    5. Your Notebook lesson item will now launch to the fresh notebook.

    Find missing work

    If your Jupyter Notebook files have disappeared, it means the course staff published a new version of a given notebook to fix problems or make improvements. Your work is still saved under the original name of the previous version of the notebook.

    To recover your work:

    1. Find your current notebook version by checking the top of the notebook window for the title
    2. In your Notebook view, click the Coursera logo
    3. Find and click the name of your previous file

    Unsaved work

    "Kernels" are the execution engines behind the Jupyter Notebook UI. As kernels time out after 90 minutes of notebook activity, be sure to save your notebooks frequently to prevent losing any work. If the kernel times out before the save, you may lose the work in your current session.

    How to tell if your kernel has timed out:

    • Error messages such as "Method Not Allowed" appear in the toolbar area.
    • The last save or auto-checkpoint time shown in the title of the notebook window has not updated recently
    • Your cells are not running or computing when you “Shift + Enter”

    To restart your kernel:

    1. Save your notebook locally to store your current progress
    2. In the notebook toolbar, click Kernel, then Restart
    3. Try testing your kernel by running a print statement in one of your notebook cells. If this is successful, you can continue to save and proceed with your work.
    4. If your notebook kernel is still timed out, try closing your browser and relaunching the notebook. When the notebook reopens, you will need to do "Cell -> Run All" or "Cell -> Run All Above" to regenerate the execution state.
  • 此课程不提供大学学分,但部分大学可能会选择接受课程证书作为学分。查看您的合作院校,了解详情。Coursera 上的在线学位Mastertrack™ 证书提供获得大学学分的机会。

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