TensorFlow vs PySyft: What are the differences?
Developers describe TensorFlow as "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. On the other hand, PySyft is detailed as "A library for encrypted, privacy preserving machine learning". It is a Python library for secure and private Deep Learning. PySyft decouples private data from model training, using Federated Learning, Differential Privacy, and Multi-Party Computation (MPC) within the main Deep Learning frameworks like PyTorch and TensorFlow.
TensorFlow and PySyft can be primarily classified as "Machine Learning" tools.
TensorFlow is an open source tool with 140K GitHub stars and 79.6K GitHub forks. Here's a link to TensorFlow's open source repository on GitHub.
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What is PySyft?
What is TensorFlow?
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