This course is part of the TensorFlow 2 for Deep Learning Specialization

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Dr Kevin Webster

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TensorFlow 2 for Deep Learning SpecializationImperial College London

About this Course

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Welcome to this course on Customising your models with TensorFlow 2!
In this course you will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence models.

You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills.

At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop a custom neural translation model from scratch.

TensorFlow is an open source machine library, and is one of the most widely used frameworks for deep learning. The release of TensorFlow 2 marks a step change in the product development, with a central focus on ease of use for all users, from beginner to advanced level. 

This course follows on directly from the previous course Getting Started with TensorFlow 2. The additional prerequisite knowledge required in order to be successful in this course is proficiency in the python programming language, (this course uses python 3), knowledge of general machine learning concepts (such as overfitting/underfitting, supervised learning tasks, validation, regularisation and model selection), and a working knowledge of the field of deep learning, including typical model architectures (MLP, CNN, RNN, ResNet), and concepts such as transfer learning, data augmentation and word embeddings.

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Shareable Certificate

Earn a Certificate upon completion

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Course 2 of 3 in the

TensorFlow 2 for Deep Learning Specialization

Intermediate Level

* Python 3

* Knowledge of general machine learning concepts

* Knowledge of the field of deep learning

Approx. 27 hours to complete

English

Skills you will gain

Tensorflow
Deep Learning
keras

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 3 in the

TensorFlow 2 for Deep Learning Specialization

Intermediate Level

* Python 3

* Knowledge of general machine learning concepts

* Knowledge of the field of deep learning

Approx. 27 hours to complete

English

Instructor

Instructor rating

4.72/5 (53 Ratings)

Dr Kevin Webster

Senior Teaching Fellow in Statistics

Faculty of Natural Sciences, Department of Mathematics

37,004 Learners

5 Courses

Offered by

Imperial College London

Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges.

Syllabus - What you will learn from this course

Content Rating88%(1,288 ratings)

Week1

Week 1

6 hours to complete

The Keras functional API

TensorFlow offers multiple levels of API for constructing deep learning models, with varying levels of control and flexibility. In this week you will learn to use the functional API for developing more flexible model architectures, including models with multiple inputs and outputs. You will also learn about Tensors and Variables, as well as accessing and using inner layers within a model. The programming assignment for this week will put these techniques this into practice with a transfer learning application on the dogs and cats image dataset.

6 hours to complete

14 videos (Total 81 min), 5 readings, 2 quizzes

14 videos

Welcome to Customising your Models with TensorFlow 22mInterview with Laurence Moroney4mThe Keras functional API5mMultiple inputs and outputs6m[Coding tutorial] Multiple inputs and outputs9mVariables5mTensors5m[Coding tutorial] Variables and Tensors8mAccessing layer Variables4mAccessing layer Tensors5m[Coding tutorial] Accessing model layers8mFreezing layers4m[Coding tutorial] Freezing layers7mWrap up and introduction to the programming assignment1m

5 readings

About Imperial College & the team10mHow to be successful in this course10mGrading policy10mAdditional readings & helpful references10mDevice placement10m

1 practice exercise

[Knowledge check] Transfer learning10m

Week2

Week 2

6 hours to complete

Data Pipeline

A flexible and efficient data pipeline is one of the most essential parts of deep learning model development. In this week you will learn a powerful workflow for loading, processing, filtering and even augmenting data on the fly using tools from Keras and the tf.data module. In the programming assignment for this week you will apply both sets of tools to implement a data pipeline for the LSUN and CIFAR-100 datasets.

6 hours to complete

12 videos (Total 93 min), 1 reading, 2 quizzes

12 videos

Welcome to week 2 - Data Pipeline2mKeras datasets3m[Coding tutorial] Keras datasets11mDataset generators7m[Coding tutorial] Dataset generators12mKeras image data augmentation5m[Coding tutorial] Keras image data augmentation10mThe Dataset class8m[Coding tutorial] The Dataset class10mTraining with Datasets7m[Coding tutorial] Training with Datasets11mWrap up and introduction to the programming assignment1m

1 reading

TensorFlow Datasets10m

1 practice exercise

[Knowledge check] Python generators15m

Week3

Week 3

6 hours to complete

Sequence Modelling

Sequence modelling tasks represent a rich and interesting class of problems, ranging from natural language tasks such as part-of-speech tagging and sentiment analysis, to forecasting of financial time series and speech audio generation. In this week you will learn how to use the recurrent neural network API in TensorFlow, as well as several useful layer types and tools for processing sequence data. In the programming assignment for this week, you will develop a generative language model on the Shakespeare dataset.

6 hours to complete

13 videos (Total 92 min)

13 videos

Welcome to week 3 - Sequence Modelling1mInterview with Doug Kelly10mPreprocessing sequence data7m[Coding tutorial] The IMDB dataset8m[Coding tutorial] Padding and masking sequence data7mThe Embedding layer4m[Coding tutorial] The Embedding layer4m[Coding tutorial] The Embedding Projector12mRecurrent neural network layers4m[Coding tutorial] Recurrent neural network layers9mStacked RNNs and the Bidirectional wrapper7m[Coding tutorial] Stacked RNNs and the Bidirectional wrapper10mWrap up and introduction to the programming assignment1m

1 practice exercise

[Knowledge check] Recurrent neural networks15m

Week4

Week 4

6 hours to complete

Model subclassing and custom training loops

For more advanced use cases of TensorFlow, it is possible to obtain a low level of control over the design and behaviour of your deep learning model, as well as the training loop itself. In this week you will learn how to exploit the Model and Layer subclassing API to develop fully flexible model architectures, as well as using the automatic differentiation tools in TensorFlow to implement custom training loops. In the programming assignment for this week you will implement these custom model building tools to develop a deep residual network.

6 hours to complete

12 videos (Total 71 min)

12 videos

Welcome to week 4 - Model subclassing and custom training loops1mModel subclassing5m[Coding tutorial] Model subclassing5mCustom layers7m[Coding tutorial] Custom layers10mAutomatic differentiation5m[Coding tutorial] Automatic differentiation6mCustom training loops7m[Coding tutorial] Custom training loops10mtf.function decorator3m[Coding tutorial] tf.function decorator5mWrap up and introduction to the programming assignment1m

Reviews

4.8

66 reviews

5 stars
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4 stars
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3 stars
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1 star
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TOP REVIEWS FROM CUSTOMISING YOUR MODELS WITH TENSORFLOW 2

by NSMay 22, 2022

highly recommend for everyone. The course and material is well designed will help you gain insight from Tensorflow and ML project workflow.

by BLJul 23, 2022

It would be better if related readings can contain some of the background knowledge.

by MMMay 23, 2021

Loved this course, loved this specialization, the team doesn't support you so you are left alone. But we may see it as a formative experience.

by FKDec 28, 2020

Excellent. This course is an extension of the 'Getting started with TensorFlow 2'. Highly recommended.

View all reviews

About the TensorFlow 2 for Deep Learning Specialization

This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow.

The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models. The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence models. The final course specialises in the increasingly important probabilistic approach to deep learning. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library. Prerequisite knowledge for this Specialization is python 3, general machine learning and deep learning concepts, and a solid foundation in probability and statistics (especially for course 3).

Frequently Asked Questions

When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. 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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Solve problems with Jupyter Notebooks
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
Rename your existing Jupyter Notebook within the individual notebook view
In the notebook view, add “?forceRefresh=true” to the end of your notebook URL
Reload the screen
You will be directed to your home Learner Workspace where you’ll see both old and new Notebook files.
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:
Find your current notebook version by checking the top of the notebook window for the title
In your Notebook view, click the Coursera logo
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:
Save your notebook locally to store your current progress
In the notebook toolbar, click Kernel, then Restart
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.
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.

Customising your models with TensorFlow 2

About this Course

Skills you will gain