Keras in the Research Community (2016-2017)

Growing Popularity: As deep learning research flourished during this period, Keras became the tool of choice for many researchers. Its simplicity allowed researchers to focus on novel ideas rather than boilerplate code.
Multiple Backend Support: In addition to Theano and TensorFlow, Keras began supporting other backends, such as Microsoft Cognitive Toolkit (CNTK) and PlaidML, a library that enabled deep learning on GPUs that were not necessarily from NVIDIA. This flexibility contributed to Keras’ widespread adoption across different platforms.
Deep Learning Milestones:
- During this period, deep learning became essential in domains like computer vision, natural language processing, and reinforcement learning. Keras played a significant role in simplifying the implementation of popular architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which were used for image recognition and sequence modeling, respectively.
Key Research and Tools: Many state-of-the-art deep learning models developed during this time used Keras as a front-end, thanks to its compatibility with Theano and TensorFlow. Papers and implementations on key topics like image classification (e.g., ResNet) and natural language understanding (e.g., LSTM networks) often provided Keras-based examples.

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