Archives for PyTorch framework
PySyft decouples private data from model training, using federated learning, differential privacy, multi-party computation (MPC) within the main deep learning framework like PyTorch, Keras and TensorFlow.
The post Difference Between PyTorch And PySyft appeared first on Analytics India Magazine.
PySyft decouples private data from model training, using federated learning, differential privacy, multi-party computation (MPC) within the main deep learning framework like PyTorch, Keras and TensorFlow.
The post Difference Between PyTorch And PySyft appeared first on Analytics India Magazine.
PySyft decouples private data from model training, using federated learning, differential privacy, multi-party computation (MPC) within the main deep learning framework like PyTorch, Keras and TensorFlow.
The post Difference Between PyTorch And PySyft appeared first on Analytics India Magazine.
Catalyst is a PyTorch framework developed with the intent of advancing research and development in the domain of deep learning. It enables code reusability, reproducibility and rapid experimentation so that users can conveniently create deep learning models and pipelines without writing another training loop. Catalyst framework is part of the PyTorch ecosystem – a collection…
The post Guide To Catalyst – A PyTorch Framework For Accelerated Deep Learning appeared first on Analytics India Magazine.
Catalyst is a PyTorch framework developed with the intent of advancing research and development in the domain of deep learning. It enables code reusability, reproducibility and rapid experimentation so that users can conveniently create deep learning models and pipelines without writing another training loop. Catalyst framework is part of the PyTorch ecosystem – a collection…
The post Guide To Catalyst – A PyTorch Framework For Accelerated Deep Learning appeared first on Analytics India Magazine.