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Titlebook: Automated Deep Learning Using Neural Network Intelligence; Develop and Design P Ivan Gridin Book 2022 Ivan Gridin 2022 Deep Learning.Automa

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發(fā)表于 2025-3-21 16:31:47 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Automated Deep Learning Using Neural Network Intelligence
期刊簡稱Develop and Design P
影響因子2023Ivan Gridin
視頻videohttp://file.papertrans.cn/167/166283/166283.mp4
發(fā)行地址Covers application of the latest scientific advances in neural network design.Presents a clear and visual representation of neural architecture search concepts.Includes boosting of PyTorch and TensorF
圖書封面Titlebook: Automated Deep Learning Using Neural Network Intelligence; Develop and Design P Ivan Gridin Book 2022 Ivan Gridin 2022 Deep Learning.Automa
影響因子.Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development.. . The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (E
Pindex Book 2022
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Hyperparameter Optimization,ML. A small change in one of the model‘s hyperparameters can significantly change its performance. Hyperparameter Optimization (HPO) is the first and most effective step in deep learning model tuning. Due to its ubiquity, Hyperparameter Optimization is sometimes regarded as synonymous with AutoML. T
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Multi-trial Neural Architecture Search,search for the optimal deep learning models, but . (.) dispels these limits. This chapter focuses on NAS, one of the most promising areas of automated deep learning. Automatic Neural Architecture Search is increasingly important in finding appropriate deep learning models. Recent researches have pro
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Model Pruning,ver, complex neural networks are computationally expensive. And not all devices have GPU processors to run deep learning models. Therefore, it would be helpful to perform model compression methods to reduce the model size and accelerate model performance without losing accuracy significantly. One of
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