Imperial College London
Customising your models with TensorFlow 2
Imperial College London

Customising your models with TensorFlow 2

This course is part of TensorFlow 2 for Deep Learning Specialization

Taught in English

Some content may not be translated

Dr Kevin Webster

Instructor: Dr Kevin Webster

13,565 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.8

(186 reviews)

|

89%

Intermediate level

Recommended experience

27 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

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Assessments

3 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(186 reviews)

|

89%

Intermediate level

Recommended experience

27 hours (approximately)
Flexible schedule
Learn at your own pace

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

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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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 5 modules in this course

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.

What's included

14 videos5 readings1 quiz1 programming assignment1 discussion prompt6 ungraded labs1 plugin

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.

What's included

12 videos1 reading1 quiz1 programming assignment8 ungraded labs

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.

What's included

13 videos1 quiz1 programming assignment7 ungraded labs

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.

What's included

12 videos1 programming assignment8 ungraded labs

In this course you have learned a powerful set of tools for developing customised deep learning models, including for sequence data, and flexible data pipelines. The Capstone Project brings many of these concepts together with a task to develop a custom neural translation model from English into German.

What's included

2 videos1 peer review1 ungraded lab1 plugin

Instructor

Instructor ratings
4.7 (56 ratings)
Dr Kevin Webster
Imperial College London
6 Courses42,775 learners

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Recommended if you're interested in Machine Learning

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4.8

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