Imperial College London

Getting started with TensorFlow 2

This course is part of TensorFlow 2 for Deep Learning Specialization

Taught in English

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Dr Kevin Webster

Instructor: Dr Kevin Webster

35,261 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.9

(550 reviews)

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96%

Intermediate level
Some related experience required
26 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

3 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.9

(550 reviews)

|

96%

Intermediate level
Some related experience required
26 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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
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There are 5 modules in this course

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

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 quizzes1 programming assignment8 ungraded labs

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 quiz1 programming assignment8 ungraded labs

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

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

Instructor

Instructor ratings
4.9 (174 ratings)
Dr Kevin Webster
Imperial College London
6 Courses42,695 learners

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