Customising your models with TensorFlow 2

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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Syllabus

Syllabus - What you will learn from this course
Week 1
The Keras functional API
Week 2
Data Pipeline
Week 3
Sequence Modelling
Week 4
Model subclassing and custom training loops
Week 5
Capstone Project

FAQ

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.

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.

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

"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.

Reviews

Good course to build on the foundational knowledge of the first course.

Awesome course, the best tensorflow advanced course

I love the way the course is constructed. Every concept is tested in a lab and you have a well organized assignment in the end of each week. Great course. I learned a lot.

The lectures are clear and the coding assignments are very relevant and practical. The final project is complex but it is very rewarding once you complete it.