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Learning Neural Networks with Tensorflow


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Learning Neural Networks with Tensorflow
Learning Neural Networks with Tensorflow
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3.5 Hours | 634 MB
Genre: eLearning | Language: English

Neural Networks are used all around us: they index photos into categories, translate text, suggest replies for emails, and beat the best games. Many people are eager to apply this knowledge to their own data, but many fail to achieve the results they expect.

In this course, we’ll start by building a simple flower recognition program, making you feel comfortable with Tensorflow, and it will teach you several important concepts in Neural Networks. Next, you’ll start working with high-dimensional uses to predict one output: 1275 molecular features you can use to predict the atomization energy of an atom. The next program we’ll create is a handwritten number recognition system trained on the famous MNIST dataset. We’ll work our way up from a simple multilayer perceptron to a state of the art Deep Convolutional Neural Network.

In the final program, estimate what a celebrity looks like, checking for new pictures to see whether a celebrity is attractive, wears a hat, has lipstick on, and many more properties that are difficult to estimate with "traditional" computer vision techniques.

After the course, you’ll not only be able to build a Neural Network for your own dataset, you’ll also be able to reason which techniques will improve your Neural Network.

Learning Neural Networks with Tensorflow

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  1. 通过Tensorflow学习神经网络 神经网络被用于我们身边的方方面面:将照片索引成分类;转换文字;对邮件回复提出建议以及打游戏。许多人都渴望吧这项技术应用在他们自己的数据是,但多数却以失败而告终。 在本教程中,我们将从开发一个简单的花朵识别程序开始,让你体验一下Tensorflow,并讲解几种重要的神经网络概念。接下来,你将开始通过高位来预测输出结果:使用1275个分子预测原子的原子化能量。下一个程序是著名的MNIST数据集中手写数字的识别。我们将通过我们的方式从一个简单的多层感知器学习深度卷积神经网络的精髓。 在最后的程序中,会估计名人的长相,通过查看新照片来检查是否名人是吸引人的,穿衣戴帽、涂抹口红以及多种难以通过传统的计算机视觉预测的属性。 学习完本教程,你不仅会开发出你自己数据集的神经网络,并且还会对哪些技术会改善你的神经网络提出想法。
    wilde(特殊组-翻译)5个月前 (12-16)