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- Large scale performance analysis of distributed deep learning . . .
This study presents a comprehensive analysis and comparison of three well-established distributed deep learning frameworks—Horovod, DeepSpeed, and Distributed Data Parallel by PyTorch—with a focus on their runtime performance and scalability
- A Data-Centric Approach to improve performance of deep . . .
Traditionally, researchers have adopted a Model-Centric Approach, focusing on developing new algorithms and models to enhance performance without altering the underlying data
- Optimizing Deep Learning Models for Enhanced Performance in . . .
This paper explores various optimization techniques that improve the efficiency, accuracy, and generalization capabilities of deep learning models
- Deep Learning: A Comprehensive Overview on Techniques . . .
In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others We also summarize real-world application areas where deep learning techniques can be used
- Deep learning modelling techniques: current progress . . .
Thus, this paper comprehensively reviews the state-of-art DL modelling techniques and provides insights into their advantages and challenges It was found that many of the models exhibit a highly domain-specific efficiency and could be trained by two or more methods
- A Comprehensive Review of Deep Learning: Architectures . . .
Deep learning (DL) has significantly transformed the field of artificial intelligence (AI), achieving excellent performance in different applications and demonstrating robust capabilities in handling vast amounts of data and complex computations [1, 2, 3]
- Unlocking Deep Learning Potential: Strategies for Business . . .
Dive into our comprehensive guide on leveraging deep learning for business success Learn effective strategies, best practices for data preparation, and how to optimize your models for groundbreaking applications in various industries
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