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书 R Deep Learning Projects: Master the techniques to design and develop neural network models in R 位于网站上免费访问和优质,所以在任何移动设备上阅读它是非常方便和舒适。 去下载 R Deep Learning Projects: Master the techniques to design and develop neural network models in R 网站上的MOBI不需要注册或付款即可访问该文件。 检查文件是否有错误和病毒,因此请确保我们网站的可下载资料的质量。 快速下载速度将有助于节省时间并尽快投入阅读。 您可以通过下载文件或在网站上在线阅读移动设备上的图书。 如果您在设备的内存中下载电子书,即使在线下时也可以随时阅读。 还要注意目录中的其他书籍,以便收集最适合您的口味和偏好的小型电子出版物,并随时随地阅读。 5 real-world projects to help you master deep learning concepts Key Features Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices Book Description R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting. What you will learn - Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec - Apply neural networks to perform handwritten digit recognition using MXNet - Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification -Implement credit card fraud detection with Autoencoders -Master reconstructing images using variational autoencoders - Wade through sentiment analysis from movie reviews - Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks - Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reductionWho this book is for Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book. Table of Contents Handwritten Digit Recognition using Convolutional Neural Networks Traffic Signs Recognition for Intelligent Vehicles Fraud Detection with Autoencoders Text Generation using Recurrent Neural Networks Sentiment Analysis with Word Embedding. 我们提供阅读或下载 R Deep Learning Projects: Master the techniques to design and develop neural network models in R 我们网站上的MOBI是完全免费的,没有注册。 本书已完整并经过病毒测试,因此,通过从我们这里下载此电子版,您可以完全享受阅读,而不会对您用于阅读的移动设备造成任何威胁。 下载不需要付费,因为本书是供用户阅读和阅读的。 如果你喜欢这本书 R Deep Learning Projects: Master the techniques to design and develop neural network models in R ,注意以同一类型编写的其他出版物。 该目录提供了丰富的出版物选择,因此您不难建立一个优秀的图书馆。

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  • 日期发布: 2018年2月22日
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