Custom Models, Layers, and Loss Functions with TensorFlow Course Syllabus

Overview: This course provides a comprehensive exploration of advanced TensorFlow techniques, focusing on building custom models, layers, and loss functions. Designed for intermediate learners, it combines theoretical understanding with hands-on implementation across five core modules and a final project. With approximately 30 hours of content, the course offers flexible pacing ideal for working professionals seeking to deepen their deep learning expertise and gain practical skills in model customization using TensorFlow.

Module 1: Functional APIs

Estimated time: 7 hours

• Compare Sequential and Functional APIs in TensorFlow
• Build models with multiple inputs using the Functional API
• Create models with multiple outputs
• Implement a Siamese network architecture

Module 2: Custom Loss Functions

Estimated time: 7 hours

• Understand the role of loss functions in model training
• Define custom loss functions in TensorFlow
• Implement contrastive loss for similarity learning
• Integrate custom losses into model compilation and training

Module 3: Custom Layers

Estimated time: 7 hours

• Create custom layers using Lambda layers
• Subclass tf.keras.layers.Layer to build new layer types
• Implement forward pass logic in custom layers
• Use custom layers within larger model architectures

Module 4: Custom Models

Estimated time: 6 hours

• Subclass the tf.keras.Model class to create custom models
• Design complex architectures like ResNet using subclassing
• Override the call method for flexible forward passes
• Integrate custom layers and models into training workflows

Module 5: Custom Callbacks

Estimated time: 3 hours

• Understand the purpose of callbacks in training loops
• Subclass tf.keras.callbacks.Callback to create custom behavior
• Implement early stopping to prevent overfitting

Module 6: Final Project

Estimated time: 10 hours

• Design and implement a custom neural network architecture
• Incorporate custom layers, loss functions, and model subclassing
• Evaluate model performance and submit a project report

Prerequisites

• Intermediate knowledge of Python programming
• Familiarity with TensorFlow and Keras fundamentals
• Basic understanding of deep learning concepts and neural networks

What You'll Be Able to Do After

• Differentiate between Functional and Sequential APIs in TensorFlow
• Build advanced models such as Siamese networks
• Create and integrate custom loss functions like contrastive loss
• Design and implement custom layers using Lambda layers and subclassing
• Develop custom models by extending the TensorFlow Model class, including architectures like ResNet