DeepLearning.AI - Custom Models, Layers, and Loss Functions with TensorFlow
- Offered byCoursera
Custom Models, Layers, and Loss Functions with TensorFlow at Coursera Overview
Duration | 31 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Custom Models, Layers, and Loss Functions with TensorFlow at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Custom Models, Layers, and Loss Functions with TensorFlow at Coursera Course details
- 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.
Custom Models, Layers, and Loss Functions with TensorFlow at Coursera Curriculum
Functional APIs
A conversation with Andrew Ng: Overview of the specialization
A conversation with Andrew Ng: Overview of course 1
Welcome to the course
Introduction to the Functional APIs
Declaring and stacking layers
Branching models
Creating a Multi-Output model
Multi-Output code walkthrough
Siamese network: a Multiple-Input model
Coding a Multi-Input Siamese network
Siamese network code walkthrough
Connect with your mentors and fellow learners on Slack!
Learn more about the Inception Model Architecture
Energy efficiency dataset
References about the Siamese network
Reference "The distance between two vectors"
Functional API
Custom Loss Functions
Welcome to Week 2
Creating a custom loss function
Coding the Huber Loss function
Huber Loss code walkthrough
Adding hyperparameters to custom loss functions
Turning loss functions into classes
Huber Object Loss code walkthrough
Contrastive Loss
Coding Contrastive Loss
Huber Loss reference
Reference: Dimensionality reduction by Learning an Invariant Mapping
Custom Loss
Custom Layers
Intro custom layers
Introduction to Lambda Layers
Custom Functions from Lambda Layers
Exploring custom Relu with Lambda Layers
Architecture of a Custom Layer
Coding your own custom Dense Layer
Training a neural network with your Custom Layer
Custom Layer code walkthrough
Activating your Custom Layer
Custom Layer with activation code walkthrough
Custom Layers
Custom Models
Intro to custom models
Complex architectures with the Functional API
Coding a Wide and Deep model
Using the Model class to simplify architectures
Understanding Residual networks
Coding a Residual network with the Model class
ResNet code walkthrough
Residual networks lectures (optional)
Custom Models
Bonus Content - Callbacks
Built-in Callbacks
Custom Callbacks
Custom Callbacks code walkthrough
TensorBoard visualization toolkit
References
Acknowledgments
Custom Models, Layers, and Loss Functions with TensorFlow at Coursera Admission Process
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