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Description: 深度学习允许由多个处理层组成的计算模型来学习具有多个抽象层次的数据表示。这些方法极大地提高了语音识别、视觉对象识别、目标检测以及药物发现和基因组学等许多领域的最新进展。深度学习发现复杂的结构在大数据集，通过使用反向传播算法来指示一台机器应该如何改变其内部参数，用于计算在每一层的代表性，从上一层的代表。深层卷积网在处理图像、视频、语音和音频方面取得了突破性进展，而递归网络则在文本和语音等连续数据上起到了作用。(Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.)
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Size: 1505280 |
Author: 飞飞花儿
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Description: 机器学习中神经网络的后向传播算法的相关推导(The derivation of backpropagation algorithm for neural networks in machine learning)
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Size: 934912 |
Author: 欧阳暄
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Description: 神经网络，前向传播，反向传播，减小loss值(Neural network, forward propagation, backpropagation, reduce loss value)
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Size: 1024 |
Author: hxqhxq
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Description: bachpropagation algorithm in matlab
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Size: 7168 |
Author: haniy
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Description: 利用BP神经网络对邮件分类，2分类，实现反向传播算法细节，非直接函数调用(BP neural network is used to classify mail, 2 classifications, to implement the details of backpropagation algorithm, non direct function call.)
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Size: 2048 |
Author: horizonch115 |
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Description: 深度学习代码CNN，用VC++ 实现，很有参考价值，神经网络权值参数反向传播实现。(Deep learning code CNN, implemented with VC++, is of great reference value, and neural network weight parameter backpropagation is realized.)
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Size: 547840 |
Author: lzpgfy |
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Description: 反向传播ANN的算法实现，里面有简单的data数据demo(Backpropagation ANN algorithm implementation, there is a simple data data demo)
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Size: 13312 |
Author: lwlei |
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Description: 使用matlab实现的底层cnn算法，包括求卷积，池化，和反向传播等操作(The underlying CNN algorithm implemented using MATLAB, including convolution, pooling, and backpropagation operations)
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Size: 244736 |
Author: lohaspig |
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Description: Biological systems are able to recognise temporal sequences of stimuli or compute
in the temporal domain. In this paper we are exploring whether a biophysical
model of a pyramidal neuron can detect and learn systematic time
delays between the spikes from di erent input neurons. In particular, we investigate
whether it is possible to reinforce pairs of synapses separated by a
dendritic propagation time delay corresponding to the arrival time di erence
of two spikes from two di erent input neurons. We examine two subthreshold
learning approaches where the rst relies on the backpropagation of EPSPs (excitatory
postsynaptic potentials) and the second on the backpropagation of a
somatic action potential, whose production is supported by a learning-enabling
background current.
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Size: 458752 |
Author: nabill |
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