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In deep learning, the learning rate plays a crucial role in determining the speed and stability of the training process. Using an appropriate learning rate scheduler can help optimize the learning rate over time, leading to improved model performance and faster convergence. TensorFlow 2.0 Keras provides a range of learning rate schedulers, including epoch-based schedulers that adjust the learning rate based on the current epoch. In this blog post, we will delve into epoch-based learning rate schedulers in TensorFlow 2.0 Keras, exploring their types, implementation, and applications. We will also provide code examples and best practices to help you effectively utilize these schedulers in your deep learning projects. Types of Epoch-Based Learning Rate Schedulers Keras offers several epoch-based learning rate schedulers, each with its own unique characteristics: ReduceLROnPlateau: Reduces the learning rate when a specified metric (e.g., validation loss) stops improving. ExponentialDecay: ...