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Custom Models, Layers, and Loss Functions with TensorFlow
  • Level Professional
  • المدة 31 ساعات hours
  • الطبع بواسطة DeepLearning.AI
  • Offered by

عن

In this course, you will: - Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network. - Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your neural network learn from training data. - Build off of existing standard layers to create custom layers for your models, customize a network layer with a lambda layer, understand the differences between them, learn what makes up a custom layer, and explore activation functions. - Build off of existing models to add custom functionality, learn how to define your own custom class instead of using the Functional or Sequential APIs, build models that can be inherited from the TensorFlow Model class, and build a residual network (ResNet) through defining a custom model class. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

الوحدات

A conversation with Andrew Ng

    • 2 Videos
Show info about module content

2 Videos

  • A conversation with Andrew Ng: Overview of the specialization
  • A conversation with Andrew Ng: Overview of course 1

Functional APIs

    • 2 Videos
    • 1 Readings
    • 1 ExternalTool
    • 1 Lab
Show info about module content

1 External Tool

  • Intake Survey

1 Labs

  • Functional API Practice

2 Videos

  • Welcome to the course
  • Introduction to the Functional APIs

1 Readings

  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!

Using the Functional APIs

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Declaring and stacking layers
  • Branching models

1 Readings

  • Learn more about the Inception Model Architecture

Creating a Multi-Output Architecture

    • 2 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Multi-output

2 Videos

  • Creating a Multi-Output model
  • Multi-Output code walkthrough

1 Readings

  • Energy efficiency dataset

Siamese Network

    • 3 Videos
    • 2 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Siamese network

3 Videos

  • Siamese network: a Multiple-Input model
  • Coding a Multi-Input Siamese network
  • Siamese network code walkthrough

2 Readings

  • References about the Siamese network
  • Reference "The distance between two vectors"

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes Week 1

Quiz: Functional API

    • 1 Assignment
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1 Assignment

  • Functional API

Assignment: Functional API

    • 1 Readings
    • 1 Programming
Show info about module content

1 Programming

  • Multiple Output Models using Keras Functional API

1 Readings

  • (Optional) Downloading your Notebook and Refreshing your Workspace

Custom loss functions

    • 4 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Huber Loss lab

4 Videos

  • Welcome to Week 2
  • Creating a custom loss function
  • Coding the Huber Loss function
  • Huber Loss code walkthrough

1 Readings

  • Huber Loss reference

Custom loss hyperparameters and classes

    • 3 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Huber Loss object

3 Videos

  • Adding hyperparameters to custom loss functions
  • Turning loss functions into classes
  • Huber Object Loss code walkthrough

Contrastive Loss

    • 2 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Contrastive loss in the siamese network

2 Videos

  • Contrastive Loss
  • Coding Contrastive Loss

1 Readings

  • Reference: Dimensionality reduction by Learning an Invariant Mapping

Lecture Notes (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Lecture Notes Week 2

Quiz

    • 1 Assignment
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1 Assignment

  • Custom Loss

Assignment: Custom Loss

    • 1 Programming
Show info about module content

1 Programming

  • Creating a custom loss function

Custom lambda layers

    • 4 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Lambda layer

4 Videos

  • Intro custom layers
  • Introduction to Lambda Layers
  • Custom Functions from Lambda Layers
  • Exploring custom Relu with Lambda Layers

Implementing Custom Layers

    • 4 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Custom dense layer

4 Videos

  • Architecture of a Custom Layer
  • Coding your own custom Dense Layer
  • Training a neural network with your Custom Layer
  • Custom Layer code walkthrough

Activating Custom Layers

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Activation in a custom layer

2 Videos

  • Activating your Custom Layer
  • Custom Layer with activation code walkthrough

Lecture Notes (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Lecture Notes Week 3

Week 3 Quiz: Custom layers

    • 1 Assignment
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1 Assignment

  • Custom Layers

Assignment: Custom Layers

    • 1 Programming
Show info about module content

1 Programming

  • Implement a Quadratic Layer

Complex Architectures with the Functional API

    • 3 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Build a basic model

3 Videos

  • Intro to custom models
  • Complex architectures with the Functional API
  • Coding a Wide and Deep model

Using the Model class to simplify complex architectures

    • 2 Videos
    • 1 Readings
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2 Videos

  • Using the Model class to simplify architectures
  • Understanding Residual networks

1 Readings

  • Residual networks lectures (optional)

Implementing ResNet

    • 2 Videos
    • 1 Lab
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1 Labs

  • Build a ResNet model

2 Videos

  • Coding a Residual network with the Model class
  • ResNet code walkthrough

Lecture Notes (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Lecture Notes Week 4

Week 4 Quiz: Custom models

    • 1 Assignment
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1 Assignment

  • Custom Models

End of Access to Lab Notebooks

    • 1 Readings
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1 Readings

  • [IMPORTANT] Reminder about end of access to Lab Notebooks

Assignment: Custom Models

    • 1 Programming
Show info about module content

1 Programming

  • Create a VGG network

Built-in Callbacks

    • 1 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Built-in Callbacks

1 Videos

  • Built-in Callbacks

1 Readings

  • TensorBoard visualization toolkit

Custom Callbacks

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Custom Callbacks

2 Videos

  • Custom Callbacks
  • Custom Callbacks code walkthrough

Lecture Notes (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Lecture Notes Week 5

Course Resources

    • 1 Readings
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1 Readings

  • References

Acknowledgments

    • 1 Readings
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1 Readings

  • Acknowledgments

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Laurence Moroney
Instructor

Laurence Moroney