DeepLearning.AI
Custom Models, Layers, and Loss Functions with TensorFlow
DeepLearning.AI

Custom Models, Layers, and Loss Functions with TensorFlow

Laurence Moroney

Instructor: Laurence Moroney

38,597 already enrolled

Gain insight into a topic and learn the fundamentals.
4.9

(1,048 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 31 hours
Learn at your own pace
96%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.9

(1,048 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 31 hours
Learn at your own pace
96%
Most learners liked this course

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the TensorFlow: Advanced Techniques Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course

Compare how the Functional API differs from the Sequential API, and see how the Functional API gives you additional flexibility in designing models. Practice using the functional API and build a Siamese network!

What's included

11 videos7 readings1 assignment1 programming assignment1 app item3 ungraded labs

Loss functions help measure how well a model is doing, and are used to help a neural network learn from the training data. Learn how to build custom loss functions, including the contrastive loss function that is used in a Siamese network.

What's included

9 videos3 readings1 assignment1 programming assignment3 ungraded labs

Custom layers give you the flexibility to implement models that use non-standard layers. Practice building off of existing standard layers to create custom layers for your models.

What's included

10 videos1 reading1 assignment1 programming assignment3 ungraded labs

You can build off of existing models to add custom functionality. This week, extend the TensorFlow Model Class to build a ResNet model!

What's included

7 videos3 readings1 assignment1 programming assignment2 ungraded labs

Custom callbacks allow you to customize what your model outputs or how it behaves during training. This week, implement a custom callback to stop training once the callback detects overfitting.

What's included

3 videos4 readings2 ungraded labs

Instructor

Instructor ratings
4.8 (328 ratings)
Laurence Moroney
DeepLearning.AI
19 Courses522,632 learners

Offered by

DeepLearning.AI

Recommended if you're interested in Machine Learning

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4.9

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