《Learning Node》第二版：服务器端开发文字版

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"Learning Node：Moving to the Server 文字版 第二版" 《Learning Node：Moving to the Server》是由Shelley Powers编著的一本关于Node.js服务器开发的书籍，适用于那些想要从客户端开发转向服务器端开发的开发者。本书旨在帮助读者理解和掌握Node.js的核心概念和技术，从而能够有效地构建高性能的网络应用。 Node.js是一个基于Chrome V8引擎的JavaScript运行环境，它允许开发者使用JavaScript进行服务器端编程。Node.js以其非阻塞I/O和事件驱动的模型而著名，这使得它在处理高并发请求时表现出色，特别适合于实时应用和数据密集型的实时应用（如聊天、协作工具或流媒体服务）。 在书中，作者详细讲解了以下Node.js的知识点： 1. **基础环境搭建**：包括安装Node.js环境，了解npm（Node包管理器）以及如何创建和管理项目依赖。 2. **核心模块**：涵盖Node.js的基础模块，如`fs`（文件系统）模块用于文件操作，`http`模块用于构建HTTP服务器，`net`模块用于TCP和UDP通信，以及`path`模块处理路径相关的任务。 3. **异步编程**：探讨Node.js的事件循环和回调函数，理解非阻塞I/O的工作原理，以及如何避免回调地狱。 4. **模块系统**：深入理解Node.js的模块系统，包括`require`和`module.exports`，以及CommonJS规范。 5. **Express框架**：介绍流行的Express框架，它是构建Web应用的快速、开放、极简的Web开发框架，它简化了路由设置、中间件使用和视图渲染。 6. **数据库集成**：讲解如何使用MongoDB或其他NoSQL数据库，以及ORM（对象关系映射）工具，如Mongoose，来与Node.js集成。 7. **错误处理**：学习如何有效地处理和报告错误，以提高应用的健壮性。 8. **测试和调试**：介绍使用Mocha、Chai等工具进行单元测试和集成测试，以及如何进行应用的调试。 9. **性能优化**：讨论如何监控和优化Node.js应用的性能，包括CPU使用率、内存管理和集群（cluster）模块的使用。 10. **安全实践**：涉及认证、授权、防止XSS和CSRF攻击，以及数据安全的最佳实践。 本书是Node.js初学者和有一定经验的开发者提升技能的良好资源，通过实际案例和练习，帮助读者将理论知识转化为实际开发能力。无论你是前端开发者希望拓宽技能范围，还是对服务器端编程感兴趣，这本书都将提供必要的指导和洞察。

Learning Node Moving to the Server-Side(2nd) 无水印pdf

2017-10-05 上传
Learning Node Moving to the Server-Side(2nd) 英文无水印pdf 第2版 pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，...

Learning.Node.Moving.to.the.Server-Side.2nd.Edition.14919

169 浏览量
Take your web development skills from browser to server with Node—and learn how to write fast, highly scalable network applications on this JavaScript-based platform. With this hands-on guide, you’...

Triangulation Learning Network: from Monocular to Stereo 3D Object Detection目前存在的主要问题

2023-09-13 上传
In summary, the main problems currently existing with Triangulation Learning Network (TLN) for monocular to stereo 3D object detection are inaccurate depth estimation, limitations of stereo vision, ...

import numpy as np def sigmoid(x): # the sigmoid function return 1/(1+np.exp(-x)) class LogisticReg(object): def __init__(self, indim=1): # initialize the parameters with all zeros # w: shape of [d+1, 1] self.w = np.zeros((indim + 1, 1)) def set_param(self, weights, bias): # helper function to set the parameters # NOTE: you need to implement this to pass the autograde. # weights: vector of shape [d, ] # bias: scaler def get_param(self): # helper function to return the parameters # NOTE: you need to implement this to pass the autograde. # returns: # weights: vector of shape [d, ] # bias: scaler def compute_loss(self, X, t): # compute the loss # X: feature matrix of shape [N, d] # t: input label of shape [N, ] # NOTE: return the average of the log-likelihood, NOT the sum. # extend the input matrix # compute the loss and return the loss X_ext = np.concatenate((X, np.ones((X.shape[0], 1))), axis=1) # compute the log-likelihood def compute_grad(self, X, t): # X: feature matrix of shape [N, d] # grad: shape of [d, 1] # NOTE: return the average gradient, NOT the sum. def update(self, grad, lr=0.001): # update the weights # by the gradient descent rule def fit(self, X, t, lr=0.001, max_iters=1000, eps=1e-7): # implement the .fit() using the gradient descent method. # args: # X: input feature matrix of shape [N, d] # t: input label of shape [N, ] # lr: learning rate # max_iters: maximum number of iterations # eps: tolerance of the loss difference # TO NOTE: # extend the input features before fitting to it. # return the weight matrix of shape [indim+1, 1] def predict_prob(self, X): # implement the .predict_prob() using the parameters learned by .fit() # X: input feature matrix of shape [N, d] # NOTE: make sure you extend the feature matrix first, # the same way as what you did in .fit() method. # returns the prediction (likelihood) of shape [N, ] def predict(self, X, threshold=0.5): # implement the .predict() using the .predict_prob() method # X: input feature matrix of shape [N, d] # returns the prediction of shape [N, ], where each element is -1 or 1. # if the probability p>threshold, we determine t=1, otherwise t=-1

2023-07-22 上传
# helper function to set the parameters # weights: vector of shape [d, ] # bias: scalar self.w[:-1] = weights.reshape(-1, 1) self.w[-1] = bias def get_param(self): # helper function to return ...

how to learn deep learning

2023-03-31 上传
As an AI language model, I can suggest the following steps to learn deep learning: 1. Learn the basics of machine learning: Before diving into deep learning, it is essential to understand the basics ...

learning node.js: a hands-on guide to building web applications in javascrip

2023-12-12 上传
《学习Node.js：构建JavaScript Web应用程序的实践指南》是一本关于使用Node.js构建Web应用程序的实用指南。Node.js是一个流行的服务器端JavaScript运行时环境，它具有非阻塞事件驱动的特性，使得构建高性能的Web...

how to learn node.js

2023-04-04 上传
1. Learn JavaScript: Node.js is built on top of JavaScript, so it's important to have a solid understanding of the language. Start by learning the basics of JavaScript, including variables, data types...

Task: - Download the UCM dataset from the official source or any reputable repository. - Preprocess the dataset by splitting it into training, validation, and test sets. Please use an 80-10-10 split, respectively. - Choose a suitable deep-learning algorithm for scene classification. You can use popular classifiers such as Convolutional Neural Networks (CNNs). - Implement the chosen algorithm in Python, using appropriate libraries. - Train the classifier using the training set and tune the hyperparameters using the validation set. - Evaluate the trained classifier on the test set using the following metrics: Accuracy: The overall classification accuracy. Precision: The ability to correctly classify positive samples. Recall: The ability to correctly identify positive samples. F1-score: The harmonic mean of precision and recall. Confusion Matrix: A table showing the number of correct and incorrect predictions for each class. Report: Prepare a one-page report summarizing the results of your scene classification experiment and the architecture of your network. Submit your Python code along with the one-page report summarizing the results. Make sure to organize your code and report in a clear and readable manner.

2023-06-09 上传
Sure, here's an outline of the steps you'll need to take to complete this task: 1. Download the UCM dataset from the official source or any reputable repository. 2. Preprocess the dataset by ...

GBDT分类模型的主要参数，训练用时，数据切分，数据洗牌，交叉验证，节点分裂评价准则，学习其数量，无放回采样比例，划分时考虑的最大特征比例，英文版

2023-05-03 上传
- Learning rate: This controls the step size at each iteration while moving toward a minimum of a loss function. - Max depth: This sets the maximum depth of each decision tree. - Max features: This is...

In neural networks, in the context of error back propagation learning, define learning rate and explain its effect on the learning process.

2023-05-16 上传
The learning rate is multiplied by the gradient of the error function with respect to the weights, and the resulting value is used to update the weights. The learning rate has a significant effect ...
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