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강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 3시간 필요

Introduction to TensorFlow

완료하는 데 3시간 필요
14개 동영상 (총 59분), 8 개의 읽기 자료
14개의 동영상
Welcome to week 11m
Hello TensorFlow!1m
[Coding tutorial] Hello TensorFlow!2m
What's new in TensorFlow 24m
Interview with Laurence Moroney5m
Introduction to Google Colab2m
[Coding tutorial] Introduction to Google Colab8m
TensorFlow documentation3m
TensorFlow installation3m
[Coding tutorial] pip installation3m
[Coding tutorial] Running TensorFlow with Docker10m
Upgrading from TensorFlow 13m
[Coding tutorial] Upgrading from TensorFlow 16m
8개의 읽기 자료
About Imperial College & the team10m
How to be successful in this course10m
Grading policy10m
Additional readings & helpful references10m
What is TensorFlow?10m
Google Colab resources10m
TensorFlow documentation10m
Upgrade TensorFlow 1.x Notebooks10m
2

2

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The Sequential model API

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13개 동영상 (총 59분)
13개의 동영상
What is Keras?1m
Building a Sequential model4m
[Coding tutorial] Building a Sequential model4m
Convolutional and pooling layers4m
[Coding tutorial] Convolutional and pooling layers5m
The compile method5m
[Coding tutorial] The compile method5m
The fit method4m
[Coding tutorial] The fit method7m
The evaluate and predict methods6m
[Coding tutorial] The evaluate and predict methods4m
Wrap up and introduction to the programming assignment1m
2개 연습문제
[Knowledge check] Feedforward and convolutional neural networks15m
[Knowledge check] Optimisers, loss functions and metrics15m
3

3

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Validation, regularisation and callbacks

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11개 동영상 (총 60분)
11개의 동영상
Interview with Andrew Ng6m
Validation sets4m
[Coding Tutorial] Validation sets9m
Model regularisation6m
[Coding Tutorial] Model regularisation4m
Introduction to callbacks5m
[Coding tutorial] Introduction to callbacks7m
Early stopping and patience6m
[Coding tutorial] Early stopping and patience5m
Wrap up and introduction to the programming assignment51
1개 연습문제
[Knowledge check] Validation and regularisation15m
4

4

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Saving and loading models

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12개 동영상 (총 74분)
12개의 동영상
Saving and loading model weights6m
[Coding tutorial] Saving and loading model weights10m
Model saving criteria4m
[Coding tutorial] Model saving criteria11m
Saving the entire model4m
[Coding tutorial] Saving the entire model8m
Loading pre-trained Keras models5m
[Coding tutorial] Loading pre-trained Keras models7m
TensorFlow Hub modules2m
[Coding tutorial] TensorFlow Hub modules8m
Wrap up and introduction to the programming assignment1m

검토

GETTING STARTED WITH TENSORFLOW 2의 최상위 리뷰

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자주 묻는 질문

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You’ll be prompted to complete an application and will be notified if you are approved. Learn more.

  • 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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