About this Course

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Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 27 hours to complete

English

Subtitles: English

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 27 hours to complete

English

Subtitles: English

Offered by

Imperial College London logo

Imperial College London

Syllabus - What you will learn from this course

Week
1

Week 1

6 hours to complete

The Keras functional API

6 hours to complete
14 videos (Total 81 min), 5 readings, 2 quizzes
14 videos
Interview with Laurence Moroney4m
The Keras functional API5m
Multiple inputs and outputs6m
[Coding tutorial] Multiple inputs and outputs9m
Variables5m
Tensors5m
[Coding tutorial] Variables and Tensors8m
Accessing layer Variables4m
Accessing layer Tensors5m
[Coding tutorial] Accessing model layers8m
Freezing layers4m
[Coding tutorial] Freezing layers7m
Wrap up and introduction to the programming assignment1m
5 readings
About Imperial College & the team10m
How to be successful in this course10m
Grading policy10m
Additional readings & helpful references10m
Device placement10m
1 practice exercise
[Knowledge check] Transfer learning10m
Week
2

Week 2

6 hours to complete

Data Pipeline

6 hours to complete
12 videos (Total 93 min), 1 reading, 2 quizzes
12 videos
Keras datasets3m
[Coding tutorial] Keras datasets11m
Dataset generators7m
[Coding tutorial] Dataset generators12m
Keras image data augmentation5m
[Coding tutorial] Keras image data augmentation10m
The Dataset class8m
[Coding tutorial] The Dataset class10m
Training with Datasets7m
[Coding tutorial] Training with Datasets11m
Wrap up and introduction to the programming assignment1m
1 reading
TensorFlow Datasets10m
1 practice exercise
[Knowledge check] Python generators15m
Week
3

Week 3

6 hours to complete

Sequence Modelling

6 hours to complete
13 videos (Total 92 min)
13 videos
Interview with Doug Kelly10m
Preprocessing sequence data7m
[Coding tutorial] The IMDB dataset8m
[Coding tutorial] Padding and masking sequence data7m
The Embedding layer4m
[Coding tutorial] The Embedding layer4m
[Coding tutorial] The Embedding Projector12m
Recurrent neural network layers4m
[Coding tutorial] Recurrent neural network layers9m
Stacked RNNs and the Bidirectional wrapper7m
[Coding tutorial] Stacked RNNs and the Bidirectional wrapper10m
Wrap up and introduction to the programming assignment1m
1 practice exercise
[Knowledge check] Recurrent neural networks15m
Week
4

Week 4

6 hours to complete

Model subclassing and custom training loops

6 hours to complete
12 videos (Total 71 min)
12 videos
Model subclassing5m
[Coding tutorial] Model subclassing5m
Custom layers7m
[Coding tutorial] Custom layers10m
Automatic differentiation5m
[Coding tutorial] Automatic differentiation6m
Custom training loops7m
[Coding tutorial] Custom training loops10m
tf.function decorator3m
[Coding tutorial] tf.function decorator5m
Wrap up and introduction to the programming assignment1m

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

More questions? Visit the Learner Help Center.