Об этом курсе

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Субтитры: Английский
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Промежуточный уровень
Прибл. 26 часов на выполнение
Субтитры: Английский


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Логотип Имперский колледж Лондона

Имперский колледж Лондона

Программа курса: что вы изучите


Неделя 1

3 ч. на завершение

Introduction to TensorFlow

3 ч. на завершение
14 видео ((всего 59 мин.)), 8 материалов для самостоятельного изучения
14 видео
Welcome to week 11мин
Hello TensorFlow!1мин
[Coding tutorial] Hello TensorFlow!2мин
What's new in TensorFlow 24мин
Interview with Laurence Moroney5мин
Introduction to Google Colab2мин
[Coding tutorial] Introduction to Google Colab8мин
TensorFlow documentation3мин
TensorFlow installation3мин
[Coding tutorial] pip installation3мин
[Coding tutorial] Running TensorFlow with Docker10мин
Upgrading from TensorFlow 13мин
[Coding tutorial] Upgrading from TensorFlow 16мин
8 материалов для самостоятельного изучения
About Imperial College & the team10мин
How to be successful in this course10мин
Grading policy10мин
Additional readings & helpful references10мин
What is TensorFlow?10мин
Google Colab resources10мин
TensorFlow documentation10мин
Upgrade TensorFlow 1.x Notebooks10мин

Неделя 2

7 ч. на завершение

The Sequential model API

7 ч. на завершение
13 видео ((всего 59 мин.))
13 видео
What is Keras?1мин
Building a Sequential model4мин
[Coding tutorial] Building a Sequential model4мин
Convolutional and pooling layers4мин
[Coding tutorial] Convolutional and pooling layers5мин
The compile method5мин
[Coding tutorial] The compile method5мин
The fit method4мин
[Coding tutorial] The fit method7мин
The evaluate and predict methods6мин
[Coding tutorial] The evaluate and predict methods4мин
Wrap up and introduction to the programming assignment1мин
2 практических упражнения
[Knowledge check] Feedforward and convolutional neural networks15мин
[Knowledge check] Optimisers, loss functions and metrics15мин

Неделя 3

7 ч. на завершение

Validation, regularisation and callbacks

7 ч. на завершение
11 видео ((всего 60 мин.))
11 видео
Interview with Andrew Ng6мин
Validation sets4мин
[Coding Tutorial] Validation sets9мин
Model regularisation6мин
[Coding Tutorial] Model regularisation4мин
Introduction to callbacks5мин
[Coding tutorial] Introduction to callbacks7мин
Early stopping and patience6мин
[Coding tutorial] Early stopping and patience5мин
Wrap up and introduction to the programming assignment51
1 практическое упражнение
[Knowledge check] Validation and regularisation15мин

Неделя 4

7 ч. на завершение

Saving and loading models

7 ч. на завершение
12 видео ((всего 74 мин.))
12 видео
Saving and loading model weights6мин
[Coding tutorial] Saving and loading model weights10мин
Model saving criteria4мин
[Coding tutorial] Model saving criteria11мин
Saving the entire model4мин
[Coding tutorial] Saving the entire model8мин
Loading pre-trained Keras models5мин
[Coding tutorial] Loading pre-trained Keras models7мин
TensorFlow Hub modules2мин
[Coding tutorial] TensorFlow Hub modules8мин
Wrap up and introduction to the programming assignment1мин



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  • 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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