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Titlebook: State-of-the-Art Deep Learning Models in TensorFlow; Modern Machine Learn David Paper Book 2021 David Paper 2021 Google Colab.Colaboratory

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發(fā)表于 2025-3-21 18:59:53 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱State-of-the-Art Deep Learning Models in TensorFlow
副標(biāo)題Modern Machine Learn
編輯David Paper
視頻videohttp://file.papertrans.cn/877/876148/876148.mp4
概述Covers state-of-the-art deep learning models that are needed for success in the field.Leverages Google’s TensorFlow-Colab Ecosystem for executing learning model applications in Python.Provides example
圖書封面Titlebook: State-of-the-Art Deep Learning Models in TensorFlow; Modern Machine Learn David Paper Book 2021 David Paper 2021 Google Colab.Colaboratory
描述.Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks...The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, obj
出版日期Book 2021
關(guān)鍵詞Google Colab; Colaboratory Cloud; TensorFlow 2; x; Deep Learning Models; Tensors; tf; data API; tf; data Data
版次1
doihttps://doi.org/10.1007/978-1-4842-7341-8
isbn_softcover978-1-4842-7340-1
isbn_ebook978-1-4842-7341-8
copyrightDavid Paper 2021
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Progressive Growing Generative Adversarial Networks,training generator models to generate large high-quality images up to about 1024 × 1024 pixels (as of this writing). The approach has proven effective at generating high-quality synthetic faces that are startlingly realistic.
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Object Detection,unding boxes around one or more effective targets located in a still image or video data. An . is the object of interest in the image or video data that is being investigated. The effective target (or targets) should be known at the beginning of the task.
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https://doi.org/10.1007/978-1-4842-7341-8Google Colab; Colaboratory Cloud; TensorFlow 2; x; Deep Learning Models; Tensors; tf; data API; tf; data Data
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TensorFlow Datasets,We introduce TensorFlow Datasets by discussing and demonstrating their many facets with code examples. Although TensorFlow Datasets are not ML models, we include this chapter because we use them in many of the chapters in this book. These datasets are created by the TensorFlow team to provide a diverse set of data for practicing ML experiments.
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