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Titlebook: Deep Learning Pipeline; Building a Deep Lear Hisham El-Amir,Mahmoud Hamdy Book 2020 Hisham El-Amir and Mahmoud Hamdy 2020 deep learning.dee

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发表于 2025-3-21 19:32:31 | 显示全部楼层 |阅读模式
书目名称Deep Learning Pipeline
副标题Building a Deep Lear
编辑Hisham El-Amir,Mahmoud Hamdy
视频video
概述Discover the difference between the regular and pipelined model of deep learning.Learn all the detailed steps of an applicable deep learning pipeline.Use a pipeline to help better manage deep learning
图书封面Titlebook: Deep Learning Pipeline; Building a Deep Lear Hisham El-Amir,Mahmoud Hamdy Book 2020 Hisham El-Amir and Mahmoud Hamdy 2020 deep learning.dee
描述.Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. .You‘ll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets.  .You‘ll also develop a deep learning project by preparing data, choosing the model that fits that data, and debugging your model to get the best fit to data all using Tensorflow techniques. Enhance your skills by accessing some of the most powerful recent trends in data science. If you‘ve ever considered building your own image or text-tagging solution or entering a Kaggle contest, .Deep Learning Pipeline. is for you! .What You‘ll Learn.Develop a deep learning project using data.Study and apply various models to your data.Debug and troubleshoot the proper m
出版日期Book 2020
关键词deep learning; deep learning pipeline; TensorFlow; Python; R programming; machine learning
版次1
doihttps://doi.org/10.1007/978-1-4842-5349-6
isbn_softcover978-1-4842-5348-9
isbn_ebook978-1-4842-5349-6
copyrightHisham El-Amir and Mahmoud Hamdy 2020
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Deep Learning Fundamentalsll try to build layers from these functions and combine these layers together to get a more complex model that will help us solve more complex problems, and all that will be described by TensorFlow examples.
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Sequential Modelsoptions. First, if they can’t understand it, they can read articles that the new article is based on, for background information. Otherwise, they . understand the new article, based on some prior knowledge of the subject, without an immediate need to read similar articles. In both cases, their abili
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Book 2020in data science. If you‘ve ever considered building your own image or text-tagging solution or entering a Kaggle contest, .Deep Learning Pipeline. is for you! .What You‘ll Learn.Develop a deep learning project using data.Study and apply various models to your data.Debug and troubleshoot the proper m
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Alexander V. Palagin,Vladimir M. Opanasenkoltilayer neural networks (both the number of layers and the number of neurons in each layer) must be limited for computational considerations, should there be any architectural changes introduced to a standard multilayer neural network to accommodate this additional constraint about the data or the network complexity?
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