[公开]北京市南水北调团城湖管理处水务综合保障-业务保障用车租赁（二次）中标公告
中标评审分析官方公示数据
中标原因深度分析独家解读
017/DeepLearning
/DeepLearning/DeepLearning/Week5/Week5-DeepLearning-Projects/1-Deep-Neural-Networks-and-optimization/1-Deep-Neural-Networks-and-optimization/1.3-Deep-Neural-Networks-and-optimization.ipynb
#1-Deep-Neural-Networks-and-optimization
- Deep Neural Networks
- Optimization of Deep Neural Networks
- Regularization
1-Deep-Neural-Networks-and-optimization
- Deep Neural Networks
- Optimization of Deep Neural Networks
- Regularization
1.1-Deep-Neural-Networks
- Deep Neural Networks
- Neural Network
- Layers
- Input Layer
- Hidden Layers
- Output Layer
- Activation Function
- Forward Propagation
- Backward Propagation
- Cost Function
- Gradient Descent
- A deep neural network is a neural network with multiple layers.
- A neural network is a machine learning model that is modeled after the human brain.
- The input layer is the layer that takes in the data.
- The hidden layers are the layers in between the input and output layers.
- The output layer is the layer that produces the final output.
- An activation function is a function that is used to introduce nonlinearity into the network.
- Forward propagation is the process of passing data through the network.
- Backward propagation is the process of adjusting the weights of the network based on the error.
- The cost function is a function that measures the error between the predicted output and the actual output.
- Gradient descent is an optimization algorithm that is used to minimize the cost function.
1.2-Optimization-of-Deep-Neural-Networks
- Optimization of Deep Neural Networks
- Gradient Descent
- Stochastic Gradient Descent
- Mini-Batch Gradient Descent
- Momentum
- Nesterov Accelerated Gradient
- Adaptive Moment Estimation (Adam)
- Learning Rate Decay
- Optimization of Deep Neural Networks refers to the process of finding the best set of weights for the network.
- Gradient Descent is an optimization algorithm that is used to minimize the cost function.
- Stochastic Gradient Descent is a variant of gradient descent that uses a single data point to update the weights.
- Mini-Batch Gradient Descent is a variant of gradient descent that uses a small batch of data points to update the weights.
- Momentum is an optimization algorithm that uses the past gradients to accelerate the convergence of the weights.
- Nesterov Accelerated Gradient is an optimization algorithm that uses the past gradients to accelerate the convergence of the weights.
- Adaptive Moment Estimation (Adam) is an optimization algorithm that adapts the learning rate for each weight.
- Learning Rate Decay is a technique that decreases the learning rate over time to prevent the weights from converging too quickly.
1.3-Regularization
- Regularization
- L1 Regularization
- L2 Regularization
- Dropout
- Weight Initialization
- Regularization is a technique that is used to prevent overfitting in deep neural networks.
- L1 regularization is a technique that adds a penalty to the cost function based on the absolute value of the weights.
- L2 regularization is a technique that adds a penalty to the cost function based on the square of the weights.
- Dropout is a technique that randomly sets a fraction of the activations to zero during training to prevent overfitting.
- Weight Initialization is the process of initializing the weights in the network to prevent overfitting.
1.3-Deep-Neural-Networks-and-optimization
- Deep Neural Networks
- Optimization of Deep Neural Networks
- Regularization
1-Deep-Neural-Networks-and-optimization
- Deep Neural Networks
- Neural Network
- Layers
- Input Layer
- Hidden Layers
- Output Layer
- Activation Function
- Forward Propagation
- Backward Propagation
- Cost Function
- Gradient Descent
- A deep neural network is a neural network with multiple layers.
- A neural network is a machine learning model that is modeled after the human brain.
- The input layer is the layer that takes in the data.
- The hidden layers are the layers in between the input and output layers.
- The output layer is the layer that produces the final output.
- An activation function is a function that is used to introduce nonlinearity into the network.
- Forward propagation is the process of passing data through the network.
- Backward propagation is the process of adjusting the weights of the network based on the error.
- The cost function is a function that measures the error between the predicted output and the actual output.
- Gradient descent is an optimization algorithm that is used to minimize the cost function.
1.2-Optimization-of-Deep-Neural-Networks
- Optimization of Deep Neural Networks
- Gradient Descent
- Stochastic Gradient Descent
- Mini-Batch Gradient Descent
- Momentum
- Nesterov Accelerated Gradient
- Adaptive Moment Estimation (Adam)
- Learning Rate Decay
- Optimization of Deep Neural Networks refers to the process of finding the best set of weights for the network.
- Gradient Des
一、项目编号：11000026210200166878-XM001
二、项目名称：北京市南水北调团城湖管理处水务综合保障-业务保障用车租赁
三、中标（成交）信息
总中标成交金额：105.98937 万元（人民币）
中标成交供应商名称、地址及中标成交金额：
中标成交供应商名称：北京信和汽车租赁有限公司
中标成交供应商地址：北京市大兴区礼贤镇东白疃路2号1层0113号（集群注册）
中标金额：105.98937万元
| 供应商名称 | 供应商地址 | 统一信用代码 | 中标金额 | 中标成交备注信息 |
|---|---|---|---|---|
| 北京信和汽车租赁有限公司 | 北京市大兴区礼贤镇东白疃路2号1层0113号（集群注册） | 911101065604393884 | 105.98937 万元 | 评审总得分(综合评分法)： 81.4 分 |
四、主要标的信息
| 供应商 | 商品名称 | 规格型号 | 数量 | 单价 | 总价 | 服务要求 |
|---|---|---|---|---|---|---|
| 北京信和汽车租赁有限公司 | 1 | 105.98937万元 | 105.98937万元 | 详见招标文件 |
需租赁业务保障用车14辆，含5辆5座车（轿车或其他小型客车），9辆7座车（小型普通客车），提供管理使用车辆燃油费及杂费等辅助服务项目。详见招标文件。
五、评审专家（单一来源采购人员）名单：
王彬、郭庆春、张美莲、史宇光、张倩
六、代理服务收费标准及金额：
本项目代理费总金额：1.5479万元（人民币）
本项目代理费收费标准：
详见招标文件
七、公告期限
自本公告发布之日起1个工作日。
八、其它补充事宜
1、招标编号：TC2619032
2、未中标供应商请在中标公告发布之日起5个工作日内联系采购代理机构办理退还投标保证金事宜，中标供应商请在采购合同签订之日起5个工作日内联系采购代理机构办理退还投标保证金事宜。
九、凡对本次公告内容提出询问，请按以下方式联系。
1.采购人信息
名 称：北京市水利工程管理中心本级
地址：北京市海淀区翠微路甲3号
联系方式：郭老师,010-61657627
2.采购代理机构信息
名 称：中招国际招标有限公司
地 址：北京市海淀区学院南路62号中关村资本大厦9层
联系方式：齐超越、邓嘉莹、刘慧敏、蒋雪娜，010-61954121、62108043
3.项目联系方式
项目联系人：齐超越、邓嘉莹、刘慧敏、蒋雪娜
电 话： 010-61954121、62108043
数据来源：查看官方原文 | 发布日期：2026-05-01