Add to ORKGDeep learning models' prediction accuracy tends to improve with the size of the model. The implications being that the amount of computational power needed to train models is continuously increasing. Distributed deep learning training tries to address this issue by spreading the computational load onto several devices. In theory, distributing computation onto N devices should give a performance improvement of xN. Yet, in reality the performance improvement is rarely xN, due to communication and other overheads. This thesis will study the communication overhead incurred when distributing deep learning training. Hopsworks is a platform designed for data science. The purpose of this work is to explore a feasible way of deploying distributed de...
Deep learning algorithms base their success on building high learning capacity models with millions ...
Training a deep neural network (DNN) with a single machine consumes much time. To accelerate the tra...
As deep learning techniques become more and more popular, there is the need to move these applicatio...
Deep Neural Networks (DNNs) enable computers to excel across many different applications such as ima...
The rapid growth of data and ever increasing model complexity of deep neural networks (DNNs) have en...
At present day, distributed computing is a widely used technique, where volunteers support different...
The demand for artificial intelligence has grown significantly over the past decade, and this growth...
The demand for artificial intelligence has grown significantly over the past decade, and this growth...
The field of deep learning has been the focus of plenty of research and development over the last y...
Federated learning allows a network of clients to train a single model together without exchanging t...
Machine learning (ML) has become a powerful building block for modern services, scientific endeavors...
Decentralized Machine Learning could address some problematic facets with Federated Learning. There ...
This thesis is done as part of a service development task of distributed deep learning on the CSC pr...
Decentralized training of deep learning models enables on-device learning over networks, as well as ...
Internet of things (IoT) blir bara större och större varje år och nya enheter läggs till hela tiden....
Deep learning algorithms base their success on building high learning capacity models with millions ...
Training a deep neural network (DNN) with a single machine consumes much time. To accelerate the tra...
As deep learning techniques become more and more popular, there is the need to move these applicatio...
Deep Neural Networks (DNNs) enable computers to excel across many different applications such as ima...
The rapid growth of data and ever increasing model complexity of deep neural networks (DNNs) have en...
At present day, distributed computing is a widely used technique, where volunteers support different...
The demand for artificial intelligence has grown significantly over the past decade, and this growth...
The demand for artificial intelligence has grown significantly over the past decade, and this growth...
The field of deep learning has been the focus of plenty of research and development over the last y...
Federated learning allows a network of clients to train a single model together without exchanging t...
Machine learning (ML) has become a powerful building block for modern services, scientific endeavors...
Decentralized Machine Learning could address some problematic facets with Federated Learning. There ...
This thesis is done as part of a service development task of distributed deep learning on the CSC pr...
Decentralized training of deep learning models enables on-device learning over networks, as well as ...
Internet of things (IoT) blir bara större och större varje år och nya enheter läggs till hela tiden....
Deep learning algorithms base their success on building high learning capacity models with millions ...
Training a deep neural network (DNN) with a single machine consumes much time. To accelerate the tra...
As deep learning techniques become more and more popular, there is the need to move these applicatio...