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Getting started with TensorFlow 2

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Getting started with TensorFlow 2

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Gain insight into a topic and learn the fundamentals.
4.9

582 reviews

Intermediate level
Some related experience required
Flexible schedule
3 weeks at 10 hours a week
Learn at your own pace
96%
Most learners liked this course

Gain insight into a topic and learn the fundamentals.
4.9

582 reviews

Intermediate level
Some related experience required
Flexible schedule
3 weeks at 10 hours a week
Learn at your own pace
96%
Most learners liked this course

Build your subject-matter expertise

This course is part of the TensorFlow 2 for Deep Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 5 modules in this course

Welcome to this course on Getting started with TensorFlow 2!

In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading 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 an image classifier deep learning 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 is intended for both users who are completely new to Tensorflow, as well as users with experience in Tensorflow 1.x. The 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/feedforward and convolutional neural networks), activation functions, output layers, and optimisation.

TensorFlow is one of the most popular libraries for deep learning, and it’s widely used today amongst researchers and professionals at all levels. In this week, you will get started with using TensorFlow on the Coursera platform and familiarise yourself with the course structure. You will also learn about some helpful resources when developing deep learning models in TensorFlow, including Google Colab. This week is really about getting everything set up, ready for diving into TensorFlow in the following week of the course.

What's included

14 videos8 readings1 discussion prompt1 ungraded lab1 plugin

14 videosTotal 59 minutes
  • Introduction to the course3 minutes
  • Welcome to week 11 minute
  • Hello TensorFlow!1 minute
  • [Coding tutorial] Hello TensorFlow!2 minutes
  • What's new in TensorFlow 24 minutes
  • Interview with Laurence Moroney6 minutes
  • Introduction to Google Colab3 minutes
  • [Coding tutorial] Introduction to Google Colab8 minutes
  • TensorFlow documentation3 minutes
  • TensorFlow installation3 minutes
  • [Coding tutorial] pip installation4 minutes
  • [Coding tutorial] Running TensorFlow with Docker10 minutes
  • Upgrading from TensorFlow 13 minutes
  • [Coding tutorial] Upgrading from TensorFlow 16 minutes
8 readingsTotal 80 minutes
  • About Imperial College & the team10 minutes
  • How to be successful in this course10 minutes
  • Grading policy10 minutes
  • Additional readings & helpful references10 minutes
  • What is TensorFlow?10 minutes
  • Google Colab resources10 minutes
  • TensorFlow documentation10 minutes
  • Upgrade TensorFlow 1.x Notebooks10 minutes
1 discussion promptTotal 10 minutes
  • Introduce yourself10 minutes
1 ungraded labTotal 15 minutes
  • [Coding tutorial] Hello TensorFlow!15 minutes
1 pluginTotal 15 minutes
  • Pre-Course Survey15 minutes

There are multiple ways to build and apply deep learning models in TensorFlow, from high-level, quick and easy-to-use APIs, to low-level operations. In this week you will learn to use the high-level Keras API for quickly building, training, evaluating and predicting from deep learning models. The programming assignment for this week will give you the opportunity to put all this into practice and develop an image classification model from scratch on the MNIST dataset of handwritten images.

What's included

13 videos2 assignments1 programming assignment8 ungraded labs

13 videosTotal 59 minutes
  • Welcome to week 2 - The Sequential model API2 minutes
  • What is Keras?2 minutes
  • Building a Sequential model5 minutes
  • [Coding tutorial] Building a Sequential model5 minutes
  • Convolutional and pooling layers5 minutes
  • [Coding tutorial] Convolutional and pooling layers6 minutes
  • The compile method6 minutes
  • [Coding tutorial] The compile method5 minutes
  • The fit method4 minutes
  • [Coding tutorial] The fit method8 minutes
  • The evaluate and predict methods6 minutes
  • [Coding tutorial] The evaluate and predict methods5 minutes
  • Wrap up and introduction to the programming assignment1 minute
2 assignmentsTotal 30 minutes
  • [Knowledge check] Feedforward and convolutional neural networks15 minutes
  • [Knowledge check] Optimisers, loss functions and metrics15 minutes
1 programming assignmentTotal 60 minutes
  • CNN classifier for the MNIST dataset60 minutes
8 ungraded labsTotal 260 minutes
  • [Coding tutorial] Building a Sequential model20 minutes
  • [Coding tutorial] Convolutional and pooling layers20 minutes
  • [Reading] Adding weight initialisers20 minutes
  • [Coding tutorial] The compile method20 minutes
  • [Reading] Metrics in Keras20 minutes
  • [Coding tutorial] The fit method20 minutes
  • [Coding tutorial] The evaluate and predict methods20 minutes
  • CNN classifier for the MNIST dataset120 minutes

Model validation and selection is an essential part of developing any machine learning model development to help prevent overfitting and improve generalisation. In this week you will learn how to use a validation dataset in a training run and apply regularisation techniques to your model. You will also learn how to use callbacks to monitor performance and perform actions according to specified criteria. In the programming assignment for this week you will put model validation and regularisation into practice on the well-known Iris dataset.

What's included

11 videos1 assignment1 programming assignment8 ungraded labs

11 videosTotal 60 minutes
  • Welcome to week 3 - Validation, regularisation and callbacks2 minutes
  • Interview with Andrew Ng7 minutes
  • Validation sets4 minutes
  • [Coding Tutorial] Validation sets10 minutes
  • Model regularisation7 minutes
  • [Coding Tutorial] Model regularisation5 minutes
  • Introduction to callbacks6 minutes
  • [Coding tutorial] Introduction to callbacks7 minutes
  • Early stopping and patience6 minutes
  • [Coding tutorial] Early stopping and patience6 minutes
  • Wrap up and introduction to the programming assignment1 minute
1 assignmentTotal 15 minutes
  • [Knowledge check] Validation and regularisation15 minutes
1 programming assignmentTotal 60 minutes
  • Model validation on the Iris dataset60 minutes
8 ungraded labsTotal 260 minutes
  • [Coding Tutorial] Validation sets20 minutes
  • [Coding Tutorial] Model regularisation20 minutes
  • [Reading] Batch normalisation layers20 minutes
  • [Coding tutorial] Introduction to callbacks20 minutes
  • [Reading] The logs dictionary20 minutes
  • [Coding tutorial] Early stopping and patience20 minutes
  • [Reading] Additional callbacks20 minutes
  • Model validation on the Iris dataset120 minutes

As part of your deep learning model development, you will need to be able to save and load TensorFlow models, possibly according to certain criteria you want to specify. In this week you will learn how to use callbacks to save models, manual saving and loading, and options that are available when saving models, including saving weights only. In addition, you will practice loading and using pre-trained deep learning models. In the programming assignment for this week you will write flexible model saving and loading implementations for a model trained on satellite images.

What's included

12 videos1 programming assignment8 ungraded labs

12 videosTotal 74 minutes
  • Welcome to week 4 - Saving and loading models2 minutes
  • Saving and loading model weights7 minutes
  • [Coding tutorial] Saving and loading model weights10 minutes
  • Model saving criteria5 minutes
  • [Coding tutorial] Model saving criteria12 minutes
  • Saving the entire model5 minutes
  • [Coding tutorial] Saving the entire model9 minutes
  • Loading pre-trained Keras models6 minutes
  • [Coding tutorial] Loading pre-trained Keras models8 minutes
  • TensorFlow Hub modules2 minutes
  • [Coding tutorial] TensorFlow Hub modules8 minutes
  • Wrap up and introduction to the programming assignment1 minute
1 programming assignmentTotal 60 minutes
  • Saving and loading models60 minutes
8 ungraded labsTotal 260 minutes
  • [Coding tutorial] Saving and loading model weights20 minutes
  • [Reading] Explanation of saved files20 minutes
  • [Coding tutorial] Model saving criteria20 minutes
  • [Coding tutorial] Saving the entire model20 minutes
  • [Reading] Saving model architecture only20 minutes
  • [Coding tutorial] Loading pre-trained Keras models20 minutes
  • [Coding tutorial] TensorFlow Hub modules20 minutes
  • Saving and loading models120 minutes

In this course you have learned an end-to-end workflow for developing deep learning models in Tensorflow. The Capstone Project gives you the opportunity to bring all of your knowledge together to develop a deep learning classifier on a labelled image dataset of street view house numbers.

What's included

2 videos1 peer review1 ungraded lab1 plugin

2 videosTotal 2 minutes
  • Welcome to the Capstone Project1 minute
  • Goodbye video1 minute
1 peer reviewTotal 60 minutes
  • Capstone Project60 minutes
1 ungraded labTotal 120 minutes
  • Capstone Project120 minutes
1 pluginTotal 15 minutes
  • Post-Course Survey15 minutes

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Imperial College London
6 Courses48,356 learners

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Showing 3 of 582

HC
·

Reviewed on May 28, 2023

Amazing course, one of the best so far. The course discusses the Tensorflow 2 framework in a very detailed and practical way. Thanks for the opportunity!

MW
·

Reviewed on Jul 22, 2023

Awesome course for the students who wanted to start the TensorFlow. Instructors are best, explained the topic in a simple word using appropriate practical examples.

BB
·

Reviewed on Jul 14, 2021

R​eally good practical course on image analysis with TF. Make sure you know the basics ahead as the main concepts are not explained, just put into practice.

Frequently asked questions

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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

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  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.

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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:

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  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.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.

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