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TensorFlow vs Cortex.dev: What are the differences?
TensorFlow: Open Source Software Library for Machine Intelligence. TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API; Cortex.dev: Deploy machine learning models in production. It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.
TensorFlow and Cortex.dev can be categorized as "Machine Learning" tools.
TensorFlow and Cortex.dev are both open source tools. It seems that TensorFlow with 137K GitHub stars and 78.3K forks on GitHub has more adoption than Cortex.dev with 1.42K GitHub stars and 69 GitHub forks.
Pros of Cortex.dev
Pros of TensorFlow
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
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Cons of Cortex.dev
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2