Keras Tuner, TensorFlow Hub, and Advanced Features (2019-Present)
- Keras Tuner: To assist developers in automating the process of hyperparameter optimization, Keras Tuner was introduced in 2019. It made it easier to search for the best model configurations (e.g., number of layers, learning rates) by simplifying the process of running multiple experiments.
- TensorFlow Hub: Keras was integrated with TensorFlow Hub, a repository of pre-trained models, enabling developers to easily fine-tune or reuse existing models for their specific tasks.
- Scalability and Distributed Training: Keras now supported distributed training on multiple GPUs or even across multiple nodes in a cluster, thanks to TensorFlow’s improvements in scalability. This opened the door for large-scale training tasks, especially in industries where training time and computational resources were critical.
- Research and Industry Use Cases: Keras continued to be the preferred tool for many AI researchers, contributing to advancements in natural language processing (NLP), computer vision, healthcare AI, robotics, and autonomous driving. Its ease of use made it ideal for both small-scale research projects and large-scale industrial applications.
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