Master Deep Learning with this fun, practical, hands on guide. With the explosion of big data deep learning is now on the radar. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. Other large corporations are quickly building out their own teams. If you want to join the ranks of today's top data scientists take advantage of this valuable book. It will help you get started. It reveals how deep learning models work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful, free R predictive analytics package. Bestselling decision scientist Dr. N.D Lewis shows you the shortcut up the steep steps to the very top. It's easier than you think. Through a simple to follow process you will learn how to build the most successful deep learning models used for learning from data. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications. If you want to accelerate your progress, discover the best in deep learning and act on what you have learned, this book is the place to get started. YOU'LL LEARN HOW TO: Understand Deep Neural Networks Use Autoencoders Unleash the power of Stacked Autoencoders Leverage the Restricted Boltzmann Machine Develop Recurrent Neural Networks Master Deep Belief Networks Everything you need to get started is contained within this book. It is your detailed, practical, tactical hands on guide - the ultimate cheat sheet for deep learning mastery. A book for everyone interested in machine learning, predictive analytic techniques, neural networks and decision science. Start building smarter models today using R! Buy the book today. Your next big breakthrough using deep learning is only a page away! Table of Contents Chapter 1 Introduction Chapter 2 Deep Neural Networks Chapter 3 Elman Neural Networks Chapter 4 Jordan Neural Netwo
2021-09-18 09:28:52 5.88MB R Deep Learning
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斯克莱恩评估 机器学习模型评估变得容易:绘图,表格,HTML报告,实验跟踪和Jupyter笔记本分析。 支持Python 3.6及更高版本。 安装 pip install sklearn-evaluation 产品特点 (混淆矩阵,特征重要性,精度调用,ROC) 报告生成( )
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猫地图 安装 点/setup.py CatMap可以通过pip直接安装: pip install --upgrade https://github.com/SUNCAT-Center/catmap/zipball/master 或下载/克隆存储库并运行 python setup.py install 从存储库根文件夹开始。 通过附加路径 要使用该软件包,请将该目录添加到PYTHONPATH中,例如在bash shell中: export PYTHONPATH=$HOME/THIS_FOLDER_PATH:$PYTHONPATH 或在cshell中: setenv PYTHONPATH $HOME/THIS_FOLDER_PATH:$PYTHONPATH 您将需要确保您正在运行python 2.5或更高版本,并且已安装了mpmath,numpy,scipy,ase和matpl
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量子计算机科学介绍 入门量子计算的书籍,非常适合入门
2021-09-15 15:52:29 2.35MB 量子计算
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量子计算简介。介绍了量子计算理论,处于需要,对量子理论也做了简要讲述。
2021-09-15 15:27:25 3.63MB quantum computin
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足球数据:足球(足球)数据集
2021-09-15 13:14:41 12.46MB data-science data-visualization dataset rstats
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Winner of the 2006 Joseph W. Goodman Book Writing Award! A comprehensive treatment of the principles, mathematics, and statistics of image science In today's visually oriented society, images play an important role in conveying messages. From seismic imaging to satellite images to medical images, our modern society would be lost without images to enhance our understanding of our health, our culture, and our world. Foundations of Image Science presents a comprehensive treatment of the principles, mathematics, and statistics needed to understand and evaluate imaging systems. The book is the first to provide a thorough treatment of the continuous-to-discrete, or CD, model of digital imaging.
2021-09-14 12:39:48 34.57MB Image Science
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使用FastAPI构建数据科学应用程序 Packt发布使用FastAPI构建数据科学应用程序
2021-09-14 11:44:28 14KB Python
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Writing_for_Science
2021-09-13 23:50:10 1.03MB Science
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IBM数据科学 为Coursera的IBM数据科学专业证书编写的代码和报告
2021-09-11 16:58:04 9.85MB JupyterNotebook
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