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TensorFlow™是一个基于数据流编程（dataflow programming）的符号数学系统，被广泛应用于各类机器学习（machine learning）算法的编程实现，其前身是谷歌的神经网络算法库DistBelief 。
Tensorflow拥有多层级结构，可部署于各类服务器、PC终端和网页并支持GPU和TPU高性能数值计算，被广泛应用于谷歌内部的产品开发和各领域的科学研究 。
TensorFlow由谷歌人工智能团队谷歌大脑（Google Brain）开发和维护，拥有包括TensorFlow Hub、TensorFlow Lite、TensorFlow Research Cloud在内的多个项目以及各类应用程序接口（Application Programming Interface, API）。自2015年11月9日起，TensorFlow依据阿帕奇授权协议（Apache 2.0 open source license）开放源代码 。
今天这一份书单，将来介绍几本关于TensorFlow的优质书籍。
TensorFlow系列书单
走向Tenso
Google的tensorflow相关学习书籍
Google的tensorflow框架学习指导书籍，包含例程，详细解释。
STK之Commu模块之三仿真卫星通信链路参数计算
考虑上述衰减的影响，可以得到卫星接收机输入端的载波功 ［C］ = ［PT］ +［GT］ +［GＲ］ －［L］ －［L1］ －［L2］ (
Windows 10 下 Nacos 3.0.2 单机版保姆级安装教程（含JDK 17配置与MySQL 8.0连接避坑）
本文提供Windows 10下Nacos 3.0.2单机版的详细安装教程，涵盖JDK 17配置、MySQL 8.0连接优化及生产级安全设置。通过步骤详解和常见问题解决方案，帮助开发者快速完成Nacos的安装与配置，确保服务发现与配置管理的高效运行。
windchill安装搭建（保姆式）
保姆式搭建windchill 材料准备 Vmware工具，用来创建和打开虚拟机的工具 安装系统的镜像文件 数据库安装文件，这里是Oracle数据库 Windchill的安装包 Vmware工具，用来安装和打开虚拟机的软件 Windowsserver2008的镜像工具，用来安装虚拟机环境的系统，如果你有win2007或win2010的也可以，在做虚拟机时，需要先安装系统。 Oracle数据库安装包 Windchill11M030的安装包 （安装包如有需要可以私信邮箱发送） 虚拟机安装 安装虚拟机VMware
yolov5深度剖析+源码debug级讲解系列（一） yolov5的架构和源码debug准备
前言 前段时间分析了yolov3的源码，这次想带着小伙伴一起把yolov5完全掌握。博主还是保持着一贯的风格，依旧采用debug级别的源码剖析。目的就是让大家可以通过一个系列的文章就把yolov5的架构、设计理念和每一行源码都弄懂。只要小伙伴可以花时间把这个系列读完，就会对yolov5的理解有所提升。 去年参加了kaggle的小麦检测比赛，yolov5在比赛的中段开始呈现霸榜趋势，超越了efficientdet。虽然最后yolov5因为权限等某些问题被kaggle官方禁用了，但是仍然体现出了其强大的能力。值
STM32+OV7670实战：5步搞定低成本人脸识别门禁系统（附完整代码）
本文详细介绍了基于STM32单片机和OV7670摄像头构建低成本离线人脸识别门禁系统的完整实战方案。文章从硬件选型、电路设计避坑入手，逐步讲解了图像采集优化、轻量级人脸检测与LBP特征提取策略，并提供了离线人脸库管理、特征比对以及系统集成的具体实现方法，附有核心代码示例，旨在帮助开发者快速实现可落地的嵌入式视觉应用。
基于深度强化学习的柔性作业车间动态调度
在追求敏捷和灵活的生产调度方面，处理不可预测的动态事件的能力变得越来越重要。与此同时，生产系统中的网络-物理融合产生了大量需要实时挖掘和分析的工业数据。为了便于这种实时控制，本研究提出了一种分层分布式的体系结构来解决动态柔性作业车间调度问题。采用双深度Q网络算法（DDQN）训练调度智能体，捕捉生产信息与调度目标之间的关系，为具有恒定任务到达的柔性作业车间实时做出调度决策。为了处理动态调度中问题的可变规格说明，提出了专门的状态和动作表示法。此外，还提出了一种代理奖励整形技术(surrogate reward
Codex 100个真实案例 - 用AI绘制CAD机械图纸（工程师看了直呼内行）
本案例将展示如何用 Codex 生成 Python 脚本，通过 `ezdxf` 库绘制标准 DXF 格式的 CAD 机械图纸。我们会从零开始，逐步实现直线、圆、圆弧、标注等基本图形元素，最终绘制出一张包含主视图、俯视图、尺寸标注、标题栏和技术要求的完整法兰盘零件图。还会演示参数化批量生成不同规格零件图和自动 BOM 物料清单，生成的 DXF 文件可直接在 AutoCAD、FreeCAD 中打开。全程用自然语言驱动 Codex，工程师看完直接上手！
毕设-YOLOv8-OBB 实现自有数据集缺陷旋转检测全过程记录
这次项目让我深入理解了 YOLOv8-OBB 的原理和使用流程。步骤内容工具数据标注制作倾斜缺陷样本格式转换从 HBB 转换为 OBB模型训练使用 Ultralytics YOLOv8 OBBCLI 命令模型测试推理与结果可视化通过 OBB 模型，我的缺陷检测系统获得了更高的精度和鲁棒性。未来我打算继续优化数据增强方式，比如引入随机旋转、仿射变换等，让模型更适应不同角度的样本。
计算机控制技术及应用
《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术，以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索，主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式，计算机控制系统的数学描述与基本分析方法，以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. 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');```[](https://bitdeli.com/free "Bitdeli Badge")
WINCC7.3破解文件
WINCC7.3硬件够破解文件，有需要的下载！WINCC7.3硬件够破解文件，有需要的下载！
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