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

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  • Functional APIs
    • 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!
  • Custom Loss Functions
    • 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.
  • Custom Layers
    • 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.
  • Custom Models
    • You can build off of existing models to add custom functionality. This week, extend the TensorFlow Model Class to build a ResNet model!
  • Bonus Content - Callbacks
    • 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.