Informações sobre o curso

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Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 26 horas para completar
Inglês
Legendas: Inglês
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 26 horas para completar
Inglês
Legendas: Inglês

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Imperial College London

Programa - O que você aprenderá com este curso

Semana
1

Semana 1

3 horas para concluir

Introduction to TensorFlow

3 horas para concluir
14 vídeos (Total 59 mín.), 8 leituras
14 videos
Welcome to week 11min
Hello TensorFlow!1min
[Coding tutorial] Hello TensorFlow!2min
What's new in TensorFlow 24min
Interview with Laurence Moroney5min
Introduction to Google Colab2min
[Coding tutorial] Introduction to Google Colab8min
TensorFlow documentation3min
TensorFlow installation3min
[Coding tutorial] pip installation3min
[Coding tutorial] Running TensorFlow with Docker10min
Upgrading from TensorFlow 13min
[Coding tutorial] Upgrading from TensorFlow 16min
8 leituras
About Imperial College & the team10min
How to be successful in this course10min
Grading policy10min
Additional readings & helpful references10min
What is TensorFlow?10min
Google Colab resources10min
TensorFlow documentation10min
Upgrade TensorFlow 1.x Notebooks10min
Semana
2

Semana 2

7 horas para concluir

The Sequential model API

7 horas para concluir
13 vídeos (Total 59 mín.)
13 videos
What is Keras?1min
Building a Sequential model4min
[Coding tutorial] Building a Sequential model4min
Convolutional and pooling layers4min
[Coding tutorial] Convolutional and pooling layers5min
The compile method5min
[Coding tutorial] The compile method5min
The fit method4min
[Coding tutorial] The fit method7min
The evaluate and predict methods6min
[Coding tutorial] The evaluate and predict methods4min
Wrap up and introduction to the programming assignment1min
2 exercícios práticos
[Knowledge check] Feedforward and convolutional neural networks15min
[Knowledge check] Optimisers, loss functions and metrics15min
Semana
3

Semana 3

7 horas para concluir

Validation, regularisation and callbacks

7 horas para concluir
11 vídeos (Total 60 mín.)
11 videos
Interview with Andrew Ng6min
Validation sets4min
[Coding Tutorial] Validation sets9min
Model regularisation6min
[Coding Tutorial] Model regularisation4min
Introduction to callbacks5min
[Coding tutorial] Introduction to callbacks7min
Early stopping and patience6min
[Coding tutorial] Early stopping and patience5min
Wrap up and introduction to the programming assignment51s
1 exercício prático
[Knowledge check] Validation and regularisation15min
Semana
4

Semana 4

7 horas para concluir

Saving and loading models

7 horas para concluir
12 vídeos (Total 74 mín.)
12 videos
Saving and loading model weights6min
[Coding tutorial] Saving and loading model weights10min
Model saving criteria4min
[Coding tutorial] Model saving criteria11min
Saving the entire model4min
[Coding tutorial] Saving the entire model8min
Loading pre-trained Keras models5min
[Coding tutorial] Loading pre-trained Keras models7min
TensorFlow Hub modules2min
[Coding tutorial] TensorFlow Hub modules8min
Wrap up and introduction to the programming assignment1min

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Perguntas Frequentes – FAQ

  • 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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