Author: tayyaba

Once a model is defined, it can be trained using the .fit() method, where you pass the input data, target labels, and the number of epochs. Keras provides features for monitoring training, such as callbacks for early stopping, learning rate scheduling, and logging.

Keras offers several pre-defined loss functions for common tasks, such as classification (categorical_crossentropy), regression (mean_squared_error), and custom loss functions. Additionally, it provides many metrics to track model performance, including accuracy, precision, recall, and custom metrics.

Keras is highly modular, meaning that models, layers, loss functions, optimizers, metrics, and more can all be independently defined and reused. This enables users to easily experiment with different architectures and techniques.

Keras is designed to minimize the cognitive load required to build deep learning models. Its clean and simple interface allows developers to build models quickly, without needing extensive knowledge of deep learning theory or complex programming. This ease of use makes Keras ideal for experimentation and quick iterations.

While Keras started as a standalone library with support for multiple backends (TensorFlow, Theano, CNTK), it is now tightly integrated with TensorFlow. This integration enables the full suite of TensorFlow’s power (like eager execution, distributed training, and TensorFlow Extended), while keeping Keras’s user-friendly API. When using Keras with TensorFlow, developers can access TensorFlow-specific features and…

Keras is a user-friendly, high-level API built to simplify the process of defining, training, and evaluating deep learning models. It abstracts many low-level operations, allowing developers to focus on high-level model design. With Keras, models can be written in a more readable and modular way, often in just a few lines of code.