
MXNet
Demystifying MXNet: A Scalable Deep Learning Framework
Understanding MXNet
MXNet is an open-source deep learning framework designed for efficiency and flexibility. Developed by the Apache Software Foundation, it provides a scalable and efficient way to train and deploy deep learning models. MXNet supports both imperative and symbolic programming, allowing for seamless integration with other popular libraries such as NumPy, SciPy, and pandas. Its origin can be traced back to researchers at Carnegie Mellon University, and it has since gained widespread adoption in the industry and academia due to its performance and versatility.
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Application of MXNet
MXNet has been utilized in a variety of projects and domains, including computer vision, natural language processing, and reinforcement learning. It has been used in the development of image recognition systems, language translation models, and autonomous vehicles. The scalability of MXNet makes it particularly well-suited for large-scale deep learning tasks, enabling efficient utilization of heterogeneous computing resources.
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References
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MXNet Official Website: https://mxnet.apache.org/
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MXNet GitHub Repository: https://github.com/apache/incubator-mxnet
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MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. Tianqi Chen, Mu Li, et al. (2015)
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Scalable Distributed DNN Training Using Compute-and-Memory-Effective Weight Sparsification. A. Ambartsoumian, S. Pal, et al. (2018)
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Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science. A. Ambartsoumian, S. Pal, et al. (2019)