Keras vs Torch: What are the differences?
# Introduction
1. **Language and Framework**: Keras is a high-level neural networks API written in Python while Torch is a scientific computing framework based on LuaJIT with an emphasis on deep learning.
2. **Community and Support**: Keras has a larger community and is backed by Google, making it more accessible to beginners with extensive documentation. Torch, on the other hand, has a smaller community but is favored by researchers for its flexibility and customization options.
3. **Ease of Use**: Keras is known for its user-friendly, modular design that is simpler to understand and implement. Torch provides more control and fine-tuning capabilities, making it suitable for experienced users looking for customization.
4. **Integration with Other Libraries**: Keras is compatible with TensorFlow, Theano, and Microsoft Cognitive Toolkit, providing a wide range of options for backend implementations. Torch integrates well with other libraries such as cuDNN, Caffe, and iTorch, offering different tools and extensions for deep learning projects.
5. **Performance and Speed**: Keras is optimized for quick experimentation and prototyping, making it efficient for smaller projects and quick model iterations. Torch, known for its speed and efficient execution, is preferred for applications requiring high performance and large dataset processing.
6. **Model Deployment**: Keras offers easier deployment options and compatibility with mobile and web platforms, making it more convenient for production deployments. Torch, although powerful, requires more effort for deployment due to its Lua scripting language and varied dependencies.
In Summary, Keras and Torch differ in language/framework, community/support, ease of use, integration with other libraries, performance/speed, and model deployment, catering to different user needs and preferences in the domain of deep learning frameworks.
