Keras adheres to best practises for lowering cognitive load by providing consistent and straightforward APIs, limiting the number of user activities required for typical use cases, and providing clear and responsive feedback in the event of a user error. Keras is simple to pick up and use as a result of this.
When dealing with unstructured data, deep learning's capacity to process vast amounts of features makes it incredibly strong. Deep learning techniques, on the other hand, may be overkill for simpler tasks because they require access to a large amount of data to be effective.
Machine learning can be thought of as a subset of deep learning. It is a field that is focused on computer algorithms learning and developing on their own. Deep learning uses artificial neural networks, which are supposed to mimic how humans think and learn, as opposed to machine learning, which uses simpler principles.
Keras is a Python-based deep learning API that runs on top of TensorFlow, a machine learning platform. It was created with the goal of allowing for quick experimentation. It's crucial to be able to go from idea to result as quickly as feasible when conducting research.