关于使用 RNN 所能取得的惊人成就,我将留待 Andrej Karpathy 的优秀博文《The Unreasonable Effectiveness of Recurrent Neural Networks》来讨论。但它们确实非常神奇。 Essential to these successes is the use of “LSTMs,” a very special kind of recurrent neural network which works, for many tasks, much...
An unrolled recurrent neural network. 这种链式的属性表明循环神经网络与序列和列表是密切相关的,这种结构很自然地适合该类型数据。 在最近的几年里,RNN在许多难题上都获得了难以置信的成功,比如语音识别、语言模型、机器翻译、图像捕捉等。Andrej Karpathy的一篇博客Unreasonable Effectiveness of Recurrent Neural Netwo...
An unrolled recurrent neural network. 这种链式的属性表明循环神经网络与序列和列表是密切相关的,这种结构很自然地适合该类型数据。 在最近的几年里,RNN在许多难题上都获得了难以置信的成功,比如语音识别、语言模型、机器翻译、图像捕捉等。Andrej Karpathy的一篇博客Unreasonable Effectiveness of Recurrent Neural Netwo...
Recurrent Neural Networks(循环神经网络) 传统的神经网络并不能做到信息持久化,用以前的信息预测当前的信息,这是传统神经网络剧的一个弊端,但是循环神经网络RNN解决了这个问题,RNN 是包含循环的网络,允许信息的持久化。 在上面的示例图中,神经网络的模块,AA,正在读取某个输入xixi,并输出一个值hihi。循环可以使得信息...
Recurrent Neural Networks 人类思维具有连贯性。当你看这篇文章时,根据你对前面词语的理解,你可以明白当前词语的意义。即是由前面的内容可以帮助理解后续的内容,体现了思维的连续性。 传统的神经网络(RNN之前的网络)在解决任务时,不能做到像人类思维一样的具有连续性,这成为它的一个主要不足。比如你想对电影不同时...
Recurrent Neural Network Language Models (RNN-LMs) have recently shown exceptional performance across a variety of applications. In this paper, we modify the architecture to perform Language Understanding, and advance the state-of-the-art for the widely used ATIS dataset. The core of our ...
Understanding Recurrent Neural Networks Using Nonequilibrium Response TheorySoon Hoe LimJournal of Machine Learning Research
In this post, you discovered LSTM recurrent neural networks in Keras and how they manage state. Specifically, you learned: How to develop a naive LSTM network for one-character to one-character prediction How to configure a naive LSTM to learn a sequence across time steps within a sample How...
Recurrent Neural Networks (RNNs), and specifically a variant with Long Short-Term Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data. However, while LSTMs provide exceptional results in practi...
Recurrent Neural Networks 人类不会每秒钟都从头开始思考。当你阅读这篇文章时,你是根据你对前几个词的理解来理解每个词的。你不会把一切都扔掉,再从头开始思考。你的思想具有持续性。 传统的神经网络无法做到这一点,这似乎是一个重大的缺陷。例如,想象一下,你想对一部电影中的每一个点发生什么样的事件进行分类...