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640UsbDebug
- 如果您用的是51等慢速的单片机可能没什么事情，当用ARM等快速的处理器作大量数据传输时可能会出现丢包的现象。在MCU连续的给主机发包的过程中，主机还没有将上一个包的数据从D12读走（就是D12的缓冲区处于满的情况），MCU又将另一个包写进去时会覆盖掉以前的。因此在每写入一个包时必须先判断D12有没有空的缓冲区，-If you use the other 51 may slow the SCM little things, When using such fast ARM processor fo
lowpower
- 从四个方面介绍了低压差线性稳压器的使用技巧-Introduced four low-dropout linear regulator of the use of skills
UTC_LD1117
- 关于低压差固定和可调正电压稳压器的介绍。有详细的解说。-With regard to fixed and adjustable low dropout positive voltage regulator introduction. A detailed explanation.
RT9163-33CG-datasheet
- The RT9163 is a positive low dropout regulator designed for applications requiring low dropout performance at full rated current. The device is available in fixed output voltage of 3.3V, 3.5V, and 5.0V. The RT9163 provides excellent regulatio
Single_Chip_Low_Dropout_Linear_Regulator
- 单片机 低压差线性调压器Single Chip Low-Dropout Linear Regulator-Single Chip Low-Dropout Linear Regulator Single Chip Low-Dropout Linear Regulator
FA-FAN5611
- Source for Low-Dropout LED Drivers for White, Blue, or any Color LED
hanzitogbk
- 实现汉字的gbk码，ascii码转换和虚拟点阵显示，可自定义输入字体,12点阵输出数据-accomplish the transferation between gbk code .ascii and Chinese characters,with the user-defined font function and 12 dots dropout.
delta
- hspice model of a low-dropout regulator
DeepNeuralNetwork20131115
- It provides deep learning tools of deep belief networks (DBNs).-Run testDNN to try! Each function includes descr iption. Please check it! It provides deep learning tools of deep belief networks (DBNs) of stacked restricted Boltzmann machines (RB
深度神经网络
- It provides deep learning tools of deep belief networks (DBNs).-Run testDNN to try! Each function includes descr iption. Please check it! It provides deep learning tools of deep belief networks (DBNs) of stacked restricted Boltzmann machines (RBMs).
MAX3232
- 本文档主要应用于RS-232串行通信相关领域，介绍RS3232芯片有关知识。-The MAX3222/MAX3232/MAX3237/MAX3241 transceivers have a proprietary low-dropout transmitter output stage enabling true RS-232 performance from a 3.0V to 5.5V supply with a dual charge pump.
NN
- 改进过的BP算法，有dropout,和weight decay项，可以设置三种激活函数。可以用来分类。-BP had improved algorithm, dropout, and weight decay term, you can set three activation functions. It can be used for classification.
Data_statistic_analysis_txt
- 代码为一个低压差线性稳压器的输出电压的统计分析，给出了输出电压在不同范围的统计分布-Change the code to a low-dropout linear regulator output voltage of statistical analysis, given the statistical distribution of the different output voltage ranges
Dropout1
- Code for Deep Learning for Detecting Robotic Grasps.Intended to be a simple codebase which will allow you to load the grasping dataset, process and whiten it, train a network, and perform grasp detection. Currently does not contain more advanced
one
- 基于叶片数字图像的植物识别是自动植物分类研究的热点。但是随着植物种类的增加，传统的分类方法由 于提取的特征比较单一或者分类器结构过于简单，导致叶片识别率较低。为此，本文提出使用纹理特征结合形状 特征进行识别，并且使用深度信念网络构架作为分类器。纹理特征通过局部二值模式、Gabor 滤波和灰度共生矩阵 方法得到。而形状特征向量由 Hu 氏不变量和傅里叶描述子组成。为了避免过拟合现象，使用“dropout”方法训练 深度信念网络。这种基于多特征融合的深度信念网络的植物识别方法-Plant based
DeepLearningDropout-master
- dropout和深度学习算法的结合使用，有详细的使用说明和数据集(Three types of layers: - C: convolutional layer (matrix map) - MP: max-pooling layer (matrix map) - F: fully connected layer (vector map) - O: output layer Convolutional Layers: - Scale: scale (size of p
XC6206P302MR_PDF_C9972_2012-11-17
- 3.0V稳压芯片,自身功耗小于1uA,线性度好，低压差(The XC6206 series are highly precise, low power consumption, high voltage, positive voltage regulators manufactured using CMOS and laser trimming technologies. The series provides large currents with a significantly small
DropOut深度网络
- 深度神经网络在测试时面对如此大的网络是很难克服过拟合问题的。 Dropout能够很好地解决这个问题。通过阻止特征检测器的共同作用来提高神经网络的性能。这种方法的关键步骤在于训练时随机丢失网络的节点单元包括与之连接的网络权值。在训练的时候，Dropout方法可以使得网络变得更为简单紧凑。在测试阶段，通过Dropout训练得到的网络能够更准确地预测网络的输出。这种方式有效的减少了网络的过拟合问题，并且比其他正则化的方法有了更明显的提升。 本文通过一个简单的实验来比较使用Dropout方法前后网络
dropout_and_minibatch
- 基于两层BP神经网络，加入dropout和softmax，输出层使用softmax，实现对手写字符库MNIST的识别，正确率达90%。(Based on the two level BP neural network, adding dropout and softmax, the output layer uses softmax to realize the recognition of handwritten character library MNIST, the accuracy ra
Dropout
- Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification 2016年CVPR的一篇论文 行人再识别方法(Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification A CVPR Paper 2016 Pedestrian Reidenti