TensorFlow Tapes

TensorFlow‘s `tf.GradientTape` is a crucial tool for automatic differentiation. The introduction of multiple tapes allows us to compute gradients with respect to multiple sources, enabling more sophisticated and intricate models.

Use Cases for Multiple Tapes:

• Weighted Gradients: Assign different weights to different parts of the model during gradient computation.
• Multi-Objective Optimization: Optimize the model based on multiple objectives simultaneously.
• Complex Architectures: Handle models with intertwined or nested architectures.

TensorFlow, a powerful open-source machine learning framework, introduces the concept of multiple tapes to facilitate the computation of gradients for complex models. In this data science project, we will explore the significance of multiple tapes and demonstrate their application in real-world scenarios.