Caffe vs MXNet: What are the differences?

# Introduction
In this comparison, we will discuss the key differences between Caffe and MXNet.

1. **Architecture**: Caffe is primarily built for deep learning tasks and is designed around the model of deep neural networks. On the other hand, MXNet is known for its flexibility and allows for more customization in building different types of neural networks, including convolutional, recurrent, and more.
2. **Scalability**: While both Caffe and MXNet can be used for distributed computing, MXNet is known for its excellent scalability, making it a preferred choice for large-scale deep learning projects. Caffe, although suitable for smaller projects, may not scale as effectively as MXNet.
3. **Language Support**: Caffe is mainly written in C++ and does not have extensive support for other programming languages. MXNet, on the other hand, supports multiple languages such as Python, C++, and Scala, making it more versatile for developers with different language preferences.
4. **Community and Support**: MXNet has a larger and more active community compared to Caffe, which results in more frequent updates, better documentation, and a wider range of supporting materials. This can be advantageous for developers looking for quick solutions and troubleshooting assistance.
5. **Backend Support**: Caffe relies heavily on CUDA for GPU acceleration, which may limit its capabilities on non-NVIDIA GPUs. In contrast, MXNet offers support for different GPU architectures, including NVIDIA, AMD, and Intel, providing more flexibility in hardware choices for deep learning tasks.

In Summary, Caffe and MXNet differ in architecture, scalability, language support, community, and backend capabilities, making them suitable for different use cases based on specific project requirements.