Gradio vs Keras: What are the differences?

1. Integration with different libraries: Gradio focuses on providing a user-friendly interface for creating machine learning applications without the need for extensive coding, while Keras is a high-level neural networks API that interfaces with deep learning libraries like TensorFlow and Theano. Keras allows for deep customization and control over the neural network architecture, making it more suitable for advanced users and researchers.

2. Ease of Use: Gradio has a simple and interactive user interface that allows users to quickly build and deploy machine learning models with minimal coding required. On the other hand, while Keras provides flexibility in designing neural networks, it requires a more in-depth understanding of deep learning concepts and coding knowledge to effectively utilize its capabilities.

3. Deployment Options: Gradio offers easy deployment options with built-in support for hosting machine learning models on the cloud, creating web applications, and integrating with third-party services like Slack and Google Sheets. In contrast, Keras focuses more on the training and testing of neural networks and leaves the deployment aspect to other tools and services.

In Summary, Gradio simplifies the process of building machine learning applications through its user-friendly interface and deployment options, while Keras provides advanced customization and control over neural network architectures for more experienced users.