Keras as a Front-End for Theano and TensorFlow (2015-2017)

Theano and TensorFlow as Backends: Keras was initially developed as a front-end interface that ran on top of Theano and TensorFlow. Both of these libraries were designed for low-level tensor operations and symbolic computation. Keras provided a user-friendly interface to work with these backends, simplifying the design of neural networks.
Initial Design Principles:
- Modularity: Keras was designed to be modular, meaning that users could easily mix and match different neural network components, such as layers, optimizers, and loss functions.
- Minimalism: Keras focused on reducing the complexity of neural network design, allowing users to create models with fewer lines of code. The goal was to remove the steep learning curve associated with deep learning.
- Extensibility: While Keras provided high-level abstractions, it still allowed advanced users to create custom layers and operations, making it flexible enough for both beginners and experts.
Wide Adoption: Keras quickly gained popularity in the deep learning community due to its ease of use and flexibility. By 2016, it had become one of the go-to libraries for researchers, startups, and larger organizations looking to develop AI models.

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