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Applied Machine Learning and Deep Learning with R

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Applied Machine Learning and Deep Learning with R
Applied Machine Learning and Deep Learning with R
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours 15M | 575 MB
Genre: eLearning | Language: English

In this course, we will examine in detail the R software, which is the most popular statistical programming language of recent years.

You will start with exploring different learning methods, clustering, classification, model evaluation methods and performance metrics. From there, you will dive into the general structure of the clustering algorithms and develop applications in the R environment by using clustering and classification algorithms for real-life problems Next, you will learn to use general definitions about artificial neural networks, and the concept of deep learning will be introduced. The elements of deep learning neural networks, types of deep learning networks, frameworks used for deep learning applications will be addressed and applications will be done with R TensorFlow package. Finally, you will dive into developing machine learning applications with SparkR, and learn to make distributed jobs on SparkR.

Applied Machine Learning and Deep Learning with R

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  1. 应用R进行机器学习和深度学习 在本教程中,我们将深入探讨R—这款近年来最流行的统计编程语言。 你将从学习不同的学习方法、集群、分类、模型评估方法和性能指标开始。然后,你将通过对真实的案例使用集群和分类算法,深入学习集群算法和在R环境中开发应用程序的通用结构。接下来,你将学习使用对于人工神经网络一般定义,以及深度学习的概念。深度学习神经网络的元素、深度学习网络的类型、用于深度学习应用的框架也会被讲到,并通过R Tensorflow包得以实现。最后,你将深入学习使用SparkR开发机器学习和应用程序,并学习在SparkR上完成分布式工作。
    wilde(特殊组-翻译)10个月前 (11-09)