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Building Unsupervised Learning Models with TensorFlow

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Building Unsupervised Learning Models with TensorFlow
Building Unsupervised Learning Models with TensorFlow
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours | 344 MB
Genre: eLearning | Language: English

Unsupervised learning techniques work with huge data sets to find patterns within the data. This course teaches you the details of clustering and autoencoding, two versatile unsupervised learning techniques, and how to implement them in TensorFlow.

Unsupervised learning techniques are powerful, but under utilized and often not well understood. In this course, Building Unsupervised Learning Models with TensorFlow, you'll learn the various characteristics and features of clustering models such as K-means clustering and hierarchical clustering. First, you'll dive into building a k-means clustering model in TensorFlow. Next, you'll discover autoencoders in detail, which are a type of artificial neural network used for unsupervised learning. Finally, you'll explore encodings or representation of data for dimensionality reduction of problems. By the end of this course, you'll have a better understanding of how you can work with unlabeled data using unsupervised learning techniques.

Building Unsupervised Learning Models with TensorFlow

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  1. TensorFlow开发无监督学习模型 无监督学习技术可以处理海量数据以发现数据中的模式。本教程会为你带来对集群和和antoencoding,这两种多用途无监督学习技术的详细讲解,以及如何在Tensorflow中实现。 无监督学习技术非常强大,但是利用率低并经常不好理解。在本教程中,你将学习各种集群模型的特性和功能,如K-means集群和层级化集群。首先,你将深入到在Tensorflow中开发一个k-means集群模式。然后,你将详细学习自动编码,这是用于无监督学习的人工神经网络的一种类型。最后,你将学习编码或用于降维问题中数据呈现。学习完本教程,你将更好地了解如何可以处理好用于非监督学习技术的非标签化数据。
    wilde(特殊组-翻译)3周前 (11-01)