課程信息

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中級
完成時間大約為26 小時
英語(English)
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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26 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 3 小時

Introduction to TensorFlow

完成時間為 3 小時
14 個視頻 (總計 59 分鐘), 8 個閱讀材料
14 個視頻
Welcome to week 11分鐘
Hello TensorFlow!1分鐘
[Coding tutorial] Hello TensorFlow!2分鐘
What's new in TensorFlow 24分鐘
Interview with Laurence Moroney5分鐘
Introduction to Google Colab2分鐘
[Coding tutorial] Introduction to Google Colab8分鐘
TensorFlow documentation3分鐘
TensorFlow installation3分鐘
[Coding tutorial] pip installation3分鐘
[Coding tutorial] Running TensorFlow with Docker10分鐘
Upgrading from TensorFlow 13分鐘
[Coding tutorial] Upgrading from TensorFlow 16分鐘
8 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
What is TensorFlow?10分鐘
Google Colab resources10分鐘
TensorFlow documentation10分鐘
Upgrade TensorFlow 1.x Notebooks10分鐘
2

2

完成時間為 7 小時

The Sequential model API

完成時間為 7 小時
13 個視頻 (總計 59 分鐘)
13 個視頻
What is Keras?1分鐘
Building a Sequential model4分鐘
[Coding tutorial] Building a Sequential model4分鐘
Convolutional and pooling layers4分鐘
[Coding tutorial] Convolutional and pooling layers5分鐘
The compile method5分鐘
[Coding tutorial] The compile method5分鐘
The fit method4分鐘
[Coding tutorial] The fit method7分鐘
The evaluate and predict methods6分鐘
[Coding tutorial] The evaluate and predict methods4分鐘
Wrap up and introduction to the programming assignment1分鐘
2 個練習
[Knowledge check] Feedforward and convolutional neural networks15分鐘
[Knowledge check] Optimisers, loss functions and metrics15分鐘
3

3

完成時間為 7 小時

Validation, regularisation and callbacks

完成時間為 7 小時
11 個視頻 (總計 60 分鐘)
11 個視頻
Interview with Andrew Ng6分鐘
Validation sets4分鐘
[Coding Tutorial] Validation sets9分鐘
Model regularisation6分鐘
[Coding Tutorial] Model regularisation4分鐘
Introduction to callbacks5分鐘
[Coding tutorial] Introduction to callbacks7分鐘
Early stopping and patience6分鐘
[Coding tutorial] Early stopping and patience5分鐘
Wrap up and introduction to the programming assignment51
1 個練習
[Knowledge check] Validation and regularisation15分鐘
4

4

完成時間為 7 小時

Saving and loading models

完成時間為 7 小時
12 個視頻 (總計 74 分鐘)
12 個視頻
Saving and loading model weights6分鐘
[Coding tutorial] Saving and loading model weights10分鐘
Model saving criteria4分鐘
[Coding tutorial] Model saving criteria11分鐘
Saving the entire model4分鐘
[Coding tutorial] Saving the entire model8分鐘
Loading pre-trained Keras models5分鐘
[Coding tutorial] Loading pre-trained Keras models7分鐘
TensorFlow Hub modules2分鐘
[Coding tutorial] TensorFlow Hub modules8分鐘
Wrap up and introduction to the programming assignment1分鐘

審閱

來自GETTING STARTED WITH TENSORFLOW 2的熱門評論

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  • 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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