Deep Neural Networks

Normalization Issues In neural network training, normalization is crucial to ensure consistent model performance and faster convergence. Below are key normalization methods: Normalization Method One E and E’ Distance metric (d) adjustments Normalization is used to transform input data, enhancing the efficiency of the network by bringing diverse features into a common scale. This approach helps in minimizing gradient issues, ensuring stable and accelerated training progress.

人工神经网络（Artificial Neural Networks，简写为ANNs）也简称为神经网络（NNs）或称作连接模型（Connection Model）。它是一种模仿动物神经网络行为特征，进行分布式并行信息处理的算法数学模型。这种网络依靠系统的复杂程度，通过调整内部大量节点之间相互连接的关系，从而达到处理信息的目的。

数据归一化将数据映射到[0,1]或[-1,1]或其他区间。为什么归一化？ 1. 输入数据单位不一样，有些数据的范围特别大，导致神经网络收敛慢，训练时间长。 2. 数据范围大的输入在模式分类中的作用可能偏大，范围小的作用可能偏小。 3. 由于输出层激活函数的值域有限制，需将数据映射到激活函数的值域。

使用离散Hopfield神经网络对模糊数字进行识别，并给出识别结果。可以调节数字的模糊程度，以检验识别效果。

在这个模拟中，我为非线性系统的零阶近似实现了RBF-NN。模拟包括蒙特卡罗模拟设置和RBF NN代码。对于系统估计，使用具有固定中心和扩展的高斯核。而RBF-NN的权重和偏差使用基于梯度下降的自适应学习算法进行优化。引文：Khan, S., Naseem, I., Togneri, R.等。电路系统信号处理(2017) 36: 1639. doi:10.1007/s00034-016-0375-7 https://link.springer.com/article/10.1007/s00034-016-0375-7

This file focuses on Convolutional Neural Networks within MATLAB. It presents 43 case studies that explore customized neural network implementation, particularly in the context of personalized neural network modeling and simulation. Each case provides detailed steps, explanations, and MATLAB code examples, allowing users to adapt and experiment with different neural network structures to achieve specific objectives.

本教程简要介绍了基于梯度下降和delta规则的反向传播算法下多层神经网络的训练及其数值实现。在MATLAB环境中模拟网络，训练它解决字符识别问题和著名的XOR问题。获得的结果非常有趣，表现出优异的性能。由于该算法是函数的近似，它适用于许多需要系统识别和模式分类的问题。关键词：神经网络，多层感知器，训练，模式识别，反向传播，delta规则，梯度下降。

(4) Start the database to MOUNT state. (5) Modify the database's archive mode (from non-archive to archive mode) SQL> alter database archive log; Database altered. (6) Open the database SQL> alter database open; Database altered. (7) Validate the correctness of archive parameter settings SQL> archive log list; Database log mode: archive mode, automatic archive: enabled, archive destination: E:\Oracle\ora92\RDBMS, earliest log sequence: 58, current log sequence: 60. SQL> The above display indicates that the database is running in archive mode and that the automatic archiving process is enabled. 642 Database Principles and Oracle Applications

ESP-DNN: Graph Convolutional Deep Neural Network for Predicting Electrostatic Potential Surfaces from DFT Calculations This repository contains trained models and code designed for generating ligands and proteins, creating electrostatic potential (ESP) surfaces that closely resemble DFT-quality molecular surfaces. The PQR files generated by our model include atomic charges and dipole-like atomic features, such as lone pairs, σ-conjugation, and p-orbitals. To generate ligand PQR files, a graph convolutional deep neural network (DNN) model was trained on about 100,000 molecules with ESP surfaces derived from DFT calculations. For proteins, parameterized charges of amino acids were used, ensuring compatibility with the ligand ESP surfaces generated by the DNN model. For more detailed methods and validation information, refer to the full documentation. System Requirements The program can only run on 64-bit Linux operating systems. Installation Instructions To run ESP-DNN, you will need to:1. Clone this repository.2. Set up Python and required dependencies.3. (Optional) Install additional packages. The package has been developed and tested with Python 2.7 and the following third-party libraries:- rdkit == 2018.09.3- keras == 2.2.4- tensorflow == 1.10.0- num

Development of Computer Networks (Understanding) Remote Terminal Connection Stage: The early stage of computer networks, where terminals were connected to a central mainframe for data access. Computer-to-Computer Network Stage: This stage marked the beginning of direct communication between computers, laying the foundation for modern networking. Computer Network Interconnection Stage: The evolution of networking where multiple networks were interconnected, forming the internet. Information Superhighway Stage: The current phase, characterized by high-speed data transfer and advanced networking technologies, forming the backbone of global communications. Content Slides