高等数学学习笔记 ☞ 数列与数列的极限

一文搞懂考研数列极限问题(概念/计算/证明)史上最强/最全总结 不管本科高数还是考研数学,数列极限问题,看这一篇文章管够,看完还不会做你来找我! 数列极限,是数列和极限两个充满不确定性的概念相混合,容易让人产生摸不着头脑,看到题目就害怕的感觉,本篇文章就按以下目录对这块儿重难点拨云见日,内容循序渐进,越往后越精彩,大家可以自行感受一下! 01 什么是数列?(掌握难度:★) 从字面意思就可以看出来:数列数列,就是将数排成队列。详细点来说,就是将一堆数按照某种规律排成一排,p.s.类似军训,教官让我们按照从矮到高(某种规律)排成一排。 这时,有个数在开小差,教官就开始点名 阅读详情
AI摘要

博客介绍了数列基本概念,包括定义、一般形式,对比了数列与集合的区别,还阐述了等差数列和等比数列的通项及求和公式。同时讲解了数列极限的定义、收敛与发散情况、性质,以及子数列的定义和性质,还提及了推理条件的相关知识。

1.  数列基本概念


1. 定义:就是以正整数集(或它的有限子集)为定义域的一列有序的数。

备注:
①:数列中的每一个数叫做这个数列的项。

②:排在第一位的数称为这个数列的第1项(通常也叫做首项),以此类推,排在第n位的数称为这个数列的第n项。

2. 一般形式:a_{1},a_{2},a_{3},...,a_{n},a_{n+1}...。记作:\left \{ a_{n} \right \},其中a_{n}称为该数列的通项,n为正整数。

备注:

①:数列中的项必须是数,它可以是实数,也可以是复数。

②:用符号\left \{ a_{n} \right \}表示数列,只不过是“借用”集合的符号而已。数列与集合的区别如下:

       集合中的元素是互异的,而数列中的项可以是相同的。

       集合中的元素是无序的,而数列中的项必须是有序的。

③:数列的通项不一定存在。

3. 等差数列与等比数列:

(1)等差数列:①:通项公式:a_{n}=a_{1}+(n-1)d。          ②:求和公式:S_{n}=\frac{n(a_{1}+a_{n})}{2}。   

(2)等比数列:①:通项公式:a_{n}=a_{1}q^{n-1}。                        ②:求和公式:S_{n}=\frac{a_{1}(1-q^{n})}{1-q}(q\neq 1)。


2.  数列的极限基本概念


1. 数列的极限定义:已知数列\left \{ x_{n} \right \},存在常数a,对于任意的\varepsilon>0(很小的一个数),若存在正整数N(指数列的第N项),

                                 当n>N时(指第N项后边的所有项),有|x_{n}- a| < \varepsilon,即(a-\varepsilon< x_{n} < a+\varepsilon )(指x_{n}的落在小区间内),

                                 那么①:称数列\left \{ x_{n} \right \}收敛于a。②:称a为数列

LS-Dyna模态分析实战:从模型构建到结果解读的全流程指南 本文提供了一份详尽的LS-Dyna模态分析实战指南。文章系统讲解了如何利用LS-Dyna内置的隐式求解器进行特征值求解,从模型构建、几何清理、材料与连接设置,到核心的边界条件与隐式求解控制(如*CONTROL_IMPLICIT_EIGENVALUE),最后完成计算并解读频率与振型结果。旨在帮助工程师在熟悉的环境中高效完成结构振动特性分析,为后续动态响应分析奠定基础。 阅读详情

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深度学习工具包 DeepLearningToolbox

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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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