@Machine_learn
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
How normalization applied to layers helps to reach faster convergence.
ArXiV: https://arxiv.org/abs/1502.03167
#NeuralNetwork #nn #normalization #DL
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
How normalization applied to layers helps to reach faster convergence.
ArXiV: https://arxiv.org/abs/1502.03167
#NeuralNetwork #nn #normalization #DL
arXiv.org
Batch Normalization: Accelerating Deep Network Training by...
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the...
@Machine_learn
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
How normalization applied to layers helps to reach faster convergence.
ArXiV: https://arxiv.org/abs/1502.03167
#NeuralNetwork #nn #normalization #DL
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
How normalization applied to layers helps to reach faster convergence.
ArXiV: https://arxiv.org/abs/1502.03167
#NeuralNetwork #nn #normalization #DL
arXiv.org
Batch Normalization: Accelerating Deep Network Training by...
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the...
Algorithms_for_Decision_Making_Mykel_J_Kochenderfer,_Tim_A_Wheeler.pdf
8 MB
Book: Algorithms for Decision Making
Authors: Mykel J. Kochenderfer, Tim A.Wheeler, and Kyle H. Wray
ISBN: Null
year: 2022
pages: 690
Tags: #Decision_Making #NN #LR
@Machine_learn
Authors: Mykel J. Kochenderfer, Tim A.Wheeler, and Kyle H. Wray
ISBN: Null
year: 2022
pages: 690
Tags: #Decision_Making #NN #LR
@Machine_learn
👍4
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Paper submitted by #DeepSeek team has generated significant attention in the AI community.
This work addresses the enhancement of reasoning capabilities in Large Language Models (LLMs) through the application of reinforcement learning techniques. The authors introduce a novel framework, DeepSeek-R1, which aims to improve LLM reasoning abilities by incorporating incentives for logical reasoning processes within their training. This integration of reinforcement learning allows LLMs to go beyond basic linguistic processing, developing sophisticated reasoning methods that can boost performance across a wide array of complex applications.
This approach has cause lots of discussions in different communities, but it definitely opens up the whole new direction of development for the research.
Paper: https://arxiv.org/abs/2501.12948
#nn #LLM
@Machine_learn
Paper submitted by #DeepSeek team has generated significant attention in the AI community.
This work addresses the enhancement of reasoning capabilities in Large Language Models (LLMs) through the application of reinforcement learning techniques. The authors introduce a novel framework, DeepSeek-R1, which aims to improve LLM reasoning abilities by incorporating incentives for logical reasoning processes within their training. This integration of reinforcement learning allows LLMs to go beyond basic linguistic processing, developing sophisticated reasoning methods that can boost performance across a wide array of complex applications.
This approach has cause lots of discussions in different communities, but it definitely opens up the whole new direction of development for the research.
Paper: https://arxiv.org/abs/2501.12948
#nn #LLM
@Machine_learn
arXiv.org
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via...
We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning...
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