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[Streaming Mpeg4] Video_Output_c DL : 0: 基于adsp的mpeg4视频解码器的实现，c语言源程序
Date : 2025-07-16 Size : 15kb User : jjaai
[Windows Develop] mp DL : 0: 这是我在操作系统实验中独立编写的一个模拟动态分区存储分配算法的程序，采用最佳适应算法。在VC++6.0下编译通过，程序的输出结果经过多次测试，没有发现问题。-This is my experiment in the operating system independent prepared a simulated dynamic partitioning storage allocation algorithm procedures, the use of best-fit algorithm. In VC++ 6.0 compiled under the adopted procedure output after several tests and found no problems.
Date : 2025-07-16 Size : 241kb User : daisichong
[Other Embeded program] ser-kybd DL : 0: IBMPC KEYBOARD An interfacing example is given showing the keyboard s protocols in action. This interfacing example uses a Rabbit 2000 MP to decode an IBM AT keyboard and output the ASCII equivalent of the key pressed
Date : 2025-07-16 Size : 1kb User : Saravanan
[AI-NN-PR] DeepLearningDropout-master DL : 2: 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 patch) - Number of output maps: outputMap - Shared weights: k - Bias: b Max-pooling layer - Scale: scale (size of patch) - Max-coordinate matrix: k (1 if max, 0 if not) Fully connected layer (dimension and number of feature maps stay the same) - Weight matrix: w - Bias: b Output layer (dimension equal to the dimension of output label) - Weight matrix: w - Bias: b Common parameters - Result: a - Delta: d)
Date : 2025-07-16 Size : 35.9mb User : 咕_噜
[DataMining] ELM_样例 DL : 0: 极限学习机（Extreme Learning Machine, ELM）用来训练单隐藏层前馈神经网络（SLFN）与传统的SLFN训练算法不同，极限学习机随机选取输入层权重和隐藏层偏置，输出层权重通过激活函数函数，依据Moore-Penrose（MP）广义逆矩阵理论计算解析求出。(Extreme learning machine (ELM) is used to train single hidden layer feedforward neural network (SLFN). Different from traditional SLFN training algorithm, elm randomly selects input layer weight and hidden layer bias, and output layer weight is calculated and analyzed according to Moore Penrose (MP) generalized inverse matrix theory through activation function function function.)
Date : 2025-07-16 Size : 2kb User : Mapleccc