課程信息

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中級

完成時間大約為27 小時

英語(English)

字幕:英語(English)

可分享的證書

完成後獲得證書

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27 小時

英語(English)

字幕:英語(English)

提供方

伦敦帝国学院 徽標

伦敦帝国学院

教學大綱 - 您將從這門課程中學到什麼

1

1

完成時間為 6 小時

The Keras functional API

完成時間為 6 小時
14 個視頻 (總計 81 分鐘), 5 個閱讀材料, 2 個測驗
14 個視頻
Interview with Laurence Moroney4分鐘
The Keras functional API5分鐘
Multiple inputs and outputs6分鐘
[Coding tutorial] Multiple inputs and outputs9分鐘
Variables5分鐘
Tensors5分鐘
[Coding tutorial] Variables and Tensors8分鐘
Accessing layer Variables4分鐘
Accessing layer Tensors5分鐘
[Coding tutorial] Accessing model layers8分鐘
Freezing layers4分鐘
[Coding tutorial] Freezing layers7分鐘
Wrap up and introduction to the programming assignment1分鐘
5 個閱讀材料
About Imperial College & the team10分鐘
How to be successful in this course10分鐘
Grading policy10分鐘
Additional readings & helpful references10分鐘
Device placement10分鐘
1 個練習
[Knowledge check] Transfer learning10分鐘
2

2

完成時間為 6 小時

Data Pipeline

完成時間為 6 小時
12 個視頻 (總計 93 分鐘), 1 個閱讀材料, 2 個測驗
12 個視頻
Keras datasets3分鐘
[Coding tutorial] Keras datasets11分鐘
Dataset generators7分鐘
[Coding tutorial] Dataset generators12分鐘
Keras image data augmentation5分鐘
[Coding tutorial] Keras image data augmentation10分鐘
The Dataset class8分鐘
[Coding tutorial] The Dataset class10分鐘
Training with Datasets7分鐘
[Coding tutorial] Training with Datasets11分鐘
Wrap up and introduction to the programming assignment1分鐘
1 個閱讀材料
TensorFlow Datasets10分鐘
1 個練習
[Knowledge check] Python generators15分鐘
3

3

完成時間為 6 小時

Sequence Modelling

完成時間為 6 小時
13 個視頻 (總計 92 分鐘)
13 個視頻
Interview with Doug Kelly10分鐘
Preprocessing sequence data7分鐘
[Coding tutorial] The IMDB dataset8分鐘
[Coding tutorial] Padding and masking sequence data7分鐘
The Embedding layer4分鐘
[Coding tutorial] The Embedding layer4分鐘
[Coding tutorial] The Embedding Projector12分鐘
Recurrent neural network layers4分鐘
[Coding tutorial] Recurrent neural network layers9分鐘
Stacked RNNs and the Bidirectional wrapper7分鐘
[Coding tutorial] Stacked RNNs and the Bidirectional wrapper10分鐘
Wrap up and introduction to the programming assignment1分鐘
1 個練習
[Knowledge check] Recurrent neural networks15分鐘
4

4

完成時間為 6 小時

Model subclassing and custom training loops

完成時間為 6 小時
12 個視頻 (總計 71 分鐘)
12 個視頻
Model subclassing5分鐘
[Coding tutorial] Model subclassing5分鐘
Custom layers7分鐘
[Coding tutorial] Custom layers10分鐘
Automatic differentiation5分鐘
[Coding tutorial] Automatic differentiation6分鐘
Custom training loops7分鐘
[Coding tutorial] Custom training loops10分鐘
tf.function decorator3分鐘
[Coding tutorial] tf.function decorator5分鐘
Wrap up and introduction to the programming assignment1分鐘

常見問題

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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.

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