Deep Learning with Python: A Hands-On Intr... pdf epub mobi txt 电子书 下载 2024

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Deep Learning with Python: A Hands-On Intr...

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发表于2024-12-15


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出版社: Apress
ISBN:9781484227657
商品编码:14509870130
包装:平装
外文名称:Deep Learning with Pyt...
出版时间:2017-04-19
页数:226
正文语种:英语

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Deep Learning with Python: A Hands-On Introduction
作者: Nikhil Ketkar;
ISBN13: 9781484227657
类型: 平装(简装书)
语种: 英语(English)
出版日期: 2017-04-19
出版社: Apress
页数: 226
重量(克): 435
尺寸: 25.4 x 17.78 x 1.3208 cm

商品简介
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.
This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.
Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments.
What You Will Learn
  • Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe
  • Gain the fundamentals of deep learning with mathematical prerequisites
  • Discover the practical considerations of large scale experiments
  • Take deep learning models to production
Who This Book Is For
Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.

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