Key Features of Keras Library
Simplicity
- Approachable and highly productive interface for solving machine learning (ML) problems.
- Consistent API covering every step of the ML workflow from data processing to deployment.
- Minimizes cognitive load with clear, concise interfaces.
Extensibility and Customizability
- Extensible for creating custom layers, loss functions, and preprocessing tasks.
- Allows building complex architectures using the functional API or writing models from scratch using subclassing.
- Adapts well to various use cases and research scenarios.
Cross-Platform Compatibility
- Runs on top of popular deep learning frameworks like TensorFlow, Theano, and CNTK.
- Provides a consistent experience across platforms, whether using a GPU or CPU.
- Enables seamless transfer of models between different backends.
Scalability and Performance
- Leverages the scalability and cross-platform capabilities of TensorFlow.
- Runs on powerful hardware, including TPUs and large GPU clusters.
- Suitable for both small-scale experiments and large-scale production systems.
Fast Experimentation
- Rapid prototyping and testing of different neural network architectures.
- High-level abstractions allow quick iteration on ideas, ideal for exploratory work.
Massive Ecosystem
- Rich ecosystem of pre-trained models, tools, and libraries.
- Supports a wide range of applications, from computer vision to natural language processing (NLP) to time series forecasting.
What is Keras?
Keras is an open-source deep-learning framework that gained attention due to its user-friendly interface. Keras offers ease of use, flexibility, and the ability to run seamlessly on top of TensorFlow. In this article, we are going to provide a comprehensive overview of Keras.
Table of Content
- Understanding Keras
- History of Keras
- Key Features of Keras Library
- How to Build a Model in Keras?
- Building Model using Sequential API
- Building Model using Functional API
- Applications of Keras
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