What will you learn in this Custom Models, Layers, and Loss Functions with TensorFlow Course
-
Differentiate between Functional and Sequential APIs in TensorFlow and build advanced models like Siamese networks.
-
Develop custom loss functions, including contrastive loss, to enhance model training.
-
Create custom layers using Lambda layers and subclassing techniques.
-
Design and implement custom models by extending the TensorFlow Model class, including architectures like ResNet.
Program Overview
1. Functional APIs
⏳ 7 hours
Explore the flexibility of the Functional API over the Sequential API and implement models with multiple inputs and outputs, such as Siamese networks.
2. Custom Loss Functions
⏳ 7 hours
Learn to create custom loss functions, including the contrastive loss function, to better measure model performance and guide training.
3. Custom Layers
⏳ 7 hours
Build custom layers by extending existing ones or using Lambda layers, and understand their role in model architecture.
4. Custom Models
⏳ 6 hours
Design custom models by subclassing the TensorFlow Model class, enabling the creation of complex architectures like ResNet.
5. Custom Callbacks
⏳ 3 hours
Implement custom callbacks to monitor and control the training process, such as early stopping to prevent overfitting.
Get certificate
Job Outlook
-
Equips learners for roles such as Machine Learning Engineer, Deep Learning Specialist, and AI Developer.
-
Applicable in industries like healthcare, finance, and technology where advanced model customization is essential.
-
Enhances employability by providing practical skills in building and deploying sophisticated TensorFlow models.
-
Supports career advancement in fields requiring expertise in custom neural network architectures and training techniques
