Deepkit vs Keras: What are the differences?
## Key Differences between Deepkit and Keras
Deepkit and Keras are both popular deep learning frameworks that offer different features and functionalities. Here are some key differences between the two:
1. **Integration with Tensorflow**: Deepkit is fully integrated with Tensorflow, providing seamless integration with all the features and capabilities of Tensorflow. On the other hand, Keras was originally developed as a high-level neural networks API and was later integrated into Tensorflow as the default API for defining neural networks.
2. **Ease of Use**: Keras is known for its simplicity and ease of use, making it the preferred choice for beginners and researchers to quickly build and prototype neural networks. Deepkit, on the other hand, offers a more comprehensive set of tools and features that may require a steeper learning curve for beginners.
3. **Model Flexibility**: Deepkit offers more flexibility in defining complex models and implementing custom layers and loss functions compared to Keras. This makes Deepkit more suitable for advanced users and researchers who need more control over the model architecture.
4. **Scalability**: Keras is more suitable for small to medium-sized projects and lacks scalability for large-scale industrial applications. Deepkit, on the other hand, is designed to handle large-scale deep learning projects and provides better support for distributed training and model deployment.
5. **Community and Support**: Keras has a larger and more active community compared to Deepkit, which results in more resources, tutorials, and community support available for users. On the other hand, Deepkit is rapidly growing its community and may catch up in terms of support and resources in the future.
6. **Performance**: Deepkit offers better performance optimization techniques and tools for speeding up training and inference compared to Keras, making it a preferred choice for users who prioritize performance in their deep learning projects.
In Summary, Deepkit and Keras differ in terms of their integration with Tensorflow, ease of use, model flexibility, scalability, community support, and performance optimization. Each framework has its own strengths and suits different use cases depending on the user's requirements and expertise level.
