15th Annual Software Development Jolt Product Excellence Awards

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15th Annual Software Development
Jolt Product Excellence Awards

Finalists

Books: General

  • Agile Project Management: Creating Innovative Products by Jim Highsmith (Addison-Wesley Professional)
  • Crystal Clear: A Human-Powered Methodology for Small Teams by Alistair Cockburn (Addison-Wesley Professional)
  • Exploiting Software: How to Break Code by Greg Hoglund and Gary McGraw (Addison-Wesley Professional )
  • Head First Design Patterns by Elisabeth Freeman, Eric Freeman, Bert Bates and Kathy Sierra (O'Reilly)
  • Hiring the Best Knowledge Workers, Techies & Nerds: The Secrets & Science of Hiring Technical People by Johanna Rothman and Gerald M. Weinberg (Dorset House)
  • Joel on Software by Joel Spolsky (Apress)
  • Refactoring to Patterns by Joshua Kerievsky (Addison-Wesley Professional)
  • Software Factories: Assembling Applications with Patterns, Models, Frameworks and Tools by Jack Greenfield, Keith Short, Steve Cook, Stuart Kent, John Crupi (Wiley)

    Books: Technical

  • Better, Faster, Lighter Java by Bruce A. Tate and Justin Gehtland (O'Reilly)
  • C++ Coding Standards: 101 Rules, Guidelines, and Best Practices by Herb Sutter and Andrei Alexandrescu (Addison-Wesley Professional )
  • Head First Servlets & JSP by Bert Bates, Kathy Sierra and Bryan Basham (O'Reilly)
  • Hibernate: A Developer's Notebook by James Elliott (O'Reilly)
  • Java Developer's Guide to Eclipse, Second Edition by Jim D'Anjou, Scott Fairbrother, Dan Kehn, John Kellerman, Pat McCarthy (Addison-Wesley Professional)
  • Mono: A Developer's Notebook by Edd Dumbill and Niel M. Bornstein (O'Reilly)
  • Perl Medic: Transforming Legacy Code by Peter J. Scott (Addison-Wesley Professional)
  • Physically Based Rendering: Theory to Implementation (The Interactive 3d Technology Series) by Matt Pharr and Greg Humphreys (Morgan Kaufmann)

    Business Integration and Data Tools

  • Compiere 5.1 (Compiere/open source)
  • DT/Studio 2.3 (Embarcadero Technologies)
  • EnterpriseTenFold (TenFold)
  • ILOG Rules for .NET (ILOG)
  • MapForce 2005 Enterprise Edition (Altova)
  • sforce 5.0 (salesforce.com)
  • Speech Server (Microsoft)
  • Toad for Oracle8.0 (Quest Software)

    Change and Configuration Management Tools

  • AccuRev 3.5 (AccuRev)
  • CodeBeamer 3.5 (Intland Software)
  • Dimensions 9.0 (Serena Software)
  • Perforce SCM (Perforce)
  • SourceForge Enterprise Edition 4.2 (VA Software)
  • Subversion 2004 (CollabNet)

    Design Tools

  • MagicDraw UML 9.0 (No Magic)
  • MetaEdit+ 4.0 (MetaCase)
  • Rational Software Architect (IBM)
  • Smart Development Environment 2.0 (Visual Paradigm International)
  • SmartDraw 2.0 (SmartDraw.com)
  • Together Designer 2005 (Borland)
  • Visual UML 4.1 (Visual Object Modelers)
  • WayPointer Highlighter 4.0 (Jaczone AB)

    Languages and Development Environments

  • Borland Delphi 2005 (Borland)
  • CodeRush 1.1 for Visual Studio (Developer Express)
  • Eclipse 3.0 (Eclipse Foundation)
  • IntelliJ IDEA4.5 (JetBrains)
  • JBoss AOP 1.0 (JBoss)
  • Python2.4 (Python.org)
  • REALbasic 5.5 for Windows Professional Edition (REAL Software)
  • Sun Java Studio Enterprise 7 (Sun Microsystems)

    Libraries, Frameworks and Components

  • Big Faceless Java PDF Library 2.0 (Big Faceless Organization)
  • ComponentOne Studio Enterprise 4.0 (ComponentOne)
  • Hibernate 2.1 (JBoss)
  • ImageGear Professional 14 (AccuSoft)
  • LEIF 2.2 (Rogue Wave)
  • Sun Java 2 Platform Standard Edition 5.0 (Sun Microsystems)
  • Xtreme Toolkit 9.51 Professional Edition (Codejock)

    Management Tools

  • Active! Focus 2.0 (Xapware Technologies)
  • Autotask 10.0 (Autotask)
  • CaliberRM2005 (Borland)
  • Cost Xpert 3.3 (Cost Xpert Group)
  • Rational Portfolio Manager (IBM)
  • Primavera IT Project Office 4.1 (Primavera Systems)
  • SpeeDEV 4.0 - LME (Lifecycle Management Enterprise) (SpeeDEV)
  • V1: Scrum1.5 (VersionOne)

    Mobile Development Tools

  • CodeWarrior for Symbian 3.0 Professional (Nokia)
  • ComponentOne 4.0 Studio Enterprise (ComponentOne)
  • Crossfire 5.5 (AppForge)
  • Flash Lite 1.1 (Macromedia)
  • J2ME Wireless Toolkit 2.2 (Sun Microsystems)

    Security Tools

  • Aladdin eSafe4 (Aladdin Knowledge Systems)
  • Aladdin eToken (Aladdin Knowledge Systems)
  • CounterPoint 1.0 (Mirage Networks)
  • Source Code Analysis 3.0 (Fortify Software)
  • Internet Security & Acceleration (ISA) Server2004 (Microsoft Corporation)
  • POPFile .22.2 (The POPFile Project; open source)

    Test: Automated Test Tools

  • Agitar Agitator and Dashboard 2.0 (Agitar)
  • Eclipse Test & Performance Tools Platform (TPTP) and Testing Tool 3.0 (the Eclipse Foundation)
  • Jmeter 2.0 (The Apache Foundation; open source)
  • LISA 2.5 (iTKO Inc.)
  • Parasoft Jtest 5.1 (Parasoft)
  • QA Wizard 2.4(Seapine)
  • TestComplete 3.0 (AutomatedQA)

    Test – Defect Tracking Tools

  • Census 6.0 (MetaQuest Software)
  • FogBugz 3.1 (Fog Creek Software)
  • JIRA 3.0 (Atlassian Software Systems)
  • OnTime 2004 for Web and Windows (Axosoft)
  • PowerTrack 2004 Add-In for Visual Studio (Axosoft)
  • TestTrack Pro 7.0 (Seapine)

    Utilities

  • AppExpress 2.5 (Endeavors Technology)
  • Captivate 2004 (Macromedia)
  • devAdvantage 2.1 (Anticipating Minds)
  • InstallShield 10.5 (Macrovision)
  • Omea Pro 1.0 (JetBrains)
  • Quest JProbe 5.2 (Quest Software)
  • Windows Installer XML 2.0 (WiX) (Microsoft)

    Web Development Tools

  • CollabNet Enterprise Edition (CollabNet)
  • Contribute 3.0 (Macromedia)
  • FlexWiki 1.8 (Microsoft Shared Source Initiative)
  • Macromedia Flex 1.5 (Macromedia)
  • NitroX 2.0 (M7 Corporation)
  • Sun Java Studio Creator (Sun Microsystems)
  • Tomcat 5.0 (The Apache Jakarta Project and leading Tomcat contributor JBoss)
  • Zend Studio 4.0 (Zend Technologies)

    Websites and Developer Networks

  • Agile Modeling Home Page (Ambysoft Inc.)
  • developer.* (DeveloperDotStar.com) (Daniel Read)
  • eBay Developers Program (eBay)
  • gantthead.com (gantthead.com)
  • IBM developerWorks (IBM)
  • Java.net (Sun Microsystems)

  • The O'Reilly Network (O'Reilly)

    第十五届Jolt奖的候选名单

    15th Annual Software Development Jolt Product Excellence Awards
    Finalists

    Books: General
    # Agile Project Management: Creating Innovative Products by Jim Highsmith (Addison-Wesley Professional)
    # Crystal Clear: A Human-Powered Methodology for Small Teams by Alistair Cockburn (Addison-Wesley Professional)
    # Exploiting Software: How to Break Code by Greg Hoglund and Gary McGraw (Addison-Wesley Professional )
    # Head First Design Patterns by Elisabeth Freeman, Eric Freeman, Bert Bates and Kathy Sierra (O'Reilly)
    # Hiring the Best Knowledge Workers, Techies & Nerds: The Secrets & Science of Hiring Technical People by Johanna Rothman and Gerald M. Weinberg (Dorset House)
    # Joel on Software by Joel Spolsky (Apress)
    # Refactoring to Patterns by Joshua Kerievsky (Addison-Wesley Professional)
    # Software Factories: Assembling Applications with Patterns, Models, Frameworks and Tools by Jack Greenfield, Keith Short, Steve Cook, Stuart Kent, John Crupi (Wiley)

    Books: Technical
    # Better, Faster, Lighter Java by Bruce A. Tate and Justin Gehtland (O'Reilly)
    # C++ Coding Standards: 101 Rules, Guidelines, and Best Practices by Herb Sutter and Andrei Alexandrescu (Addison-Wesley Professional )
    # Head First Servlets & JSP by Bert Bates, Kathy Sierra and Bryan Basham (O'Reilly)
    # Hibernate: A Developer's Notebook by James Elliott (O'Reilly)
    # Java Developer's Guide to Eclipse, Second Edition by Jim D'Anjou, Scott Fairbrother, Dan Kehn, John Kellerman, Pat McCarthy (Addison-Wesley Professional)
    # Mono: A Developer's Notebook by Edd Dumbill and Niel M. Bornstein (O'Reilly)
    # Perl Medic: Transforming Legacy Code by Peter J. Scott (Addison-Wesley Professional)
    # Physically Based Rendering: Theory to Implementation (The Interactive 3d Technology Series) by Matt Pharr and Greg Humphreys (Morgan Kaufmann)

    Business Integration and Data Tools
    # Compiere 5.1 (Compiere/open source)
    # DT/Studio 2.3 (Embarcadero Technologies)
    # EnterpriseTenFold (TenFold)
    # ILOG Rules for .NET (ILOG)
    # MapForce 2005 Enterprise Edition (Altova)
    # sforce 5.0 (salesforce.com)
    # Speech Server (Microsoft)
    # Toad for Oracle8.0 (Quest Software)

    Change and Configuration Management Tools
    # AccuRev 3.5 (AccuRev)
    # CodeBeamer 3.5 (Intland Software)
    # Dimensions 9.0 (Serena Software)
    # SCM 2004 (Perforce)
    # SourceForge Enterprise Edition 4.2 (VA Software)
    # Subversion 2004 (CollabNet)

    Design Tools
    # MagicDraw UML 8.0 (No Magic)
    # MetaEdit+ 4.0 (MetaCase)
    # Rational Software Architect (IBM)
    # Smart Development Environment 2.0 (Visual Paradigm International)
    # SmartDraw 2.0 (SmartDraw.com)
    # Together Designer 2005 (Borland)
    # Visual UML 4.1 (Visual Object Modelers)
    # WayPointer Highlighter 4.0 (Jaczone AB)

    Languages and Development Environments
    # Borland Delphi 2005 (Borland)
    # CodeRush 1.1 for Visual Studio (Developer Express)
    # Eclipse 3.0 (Eclipse Foundation)
    # IntelliJ IDEA4.5 (JetBrains)
    # JBoss AOP 1.0 (JBoss)
    # Python2.4 (Python.org)
    # REALbasic 5.5 for Windows Professional Edition (REAL Software)
    # Sun Java Studio Enterprise 7 (Sun Microsystems)

    Libraries, Frameworks and Components
    # Big Faceless Java PDF Library 2.0 (Big Faceless Organization)
    # ComponentOne Studio Enterprise 4.0 (ComponentOne)
    # Hibernate 2.1 (JBoss)
    # ImageGear Professional 14 (AccuSoft)
    # LEIF 2.2 (Rogue Wave)
    # Sun Java 2 Platform Standard Edition 5.0 (Sun Microsystems)
    # Xtreme Toolkit 9.51 Professional Edition (Codejock)

    Management Tools
    # Active! Focus 2.0 (Xapware Technologies)
    # Autotask 10.0 (Autotask)
    # CaliberRM2005 (Borland)
    # Cost Xpert 3.3 (Cost Xpert Group)
    # Rational Portfolio Manager (IBM)
    # Primavera IT Project Office 4.1 (Primavera Systems)
    # SpeeDEV 4.0 - LME (Lifecycle Management Enterprise) (SpeeDEV)
    # V1: Scrum1.5 (VersionOne)

    Mobile Development Tools
    # Borland C++ 2005 (Borland)
    # CodeWarrior for Symbian 3.0 Professional (Nokia)
    # ComponentOne 4.0 Studio Enterprise (ComponentOne)
    # Crossfire 5.5 (AppForge)
    # Flash Lite 1.1 (Macromedia)
    # J2ME Wireless Toolkit 2.2 (Sun Microsystems)

    Security Tools
    # Aladdin eSafe4 (Aladdin Knowledge Systems)
    # Aladdin eToken (Aladdin Knowledge Systems)
    # CounterPoint 1.0 (Mirage Networks)
    # Source Code Analysis 3.0 (Fortify Software)
    # Internet Security & Acceleration (ISA) Server2004 (Microsoft Corporation)
    # POPFile .22.2 (The POPFile Project; open source)

    Test: Automated Test Tools
    # Agitar Agitator and Dashboard 2.0 (Agitar)
    # Eclipse Test & Performance Tools Platform (TPTP) and Testing Tool 3.0 (the Eclipse Foundation)
    # Jmeter 2.0 (The Apache Foundation; open source)
    # LISA 2.5 (iTKO Inc.)
    # Parasoft Jtest 5.1 (Parasoft)
    # QA Wizard 2.4(Seapine)
    # TestComplete 3.0 (AutomatedQA)

    Test - Defect Tracking Tools
    # Census 6.0 (MetaQuest Software)
    # FogBugz 3.1 (Fog Creek Software)
    # JIRA 3.0 (Atlassian Software Systems)
    # OnTime 2004 for Web and Windows (Axosoft)
    # PowerTrack 2004 Add-In for Visual Studio (Axosoft)
    # TestTrack Pro 7.0 (Seapine)

    Utilities
    # AppExpress 2.5 (Endeavors Technology)
    # Captivate 2004 (Macromedia)
    # devAdvantage 2.1 (Anticipating Minds)
    # InstallShield 10.5 (Macrovision)
    # Omea Pro 1.0 (JetBrains)
    # Quest JProbe 5.2 (Quest Software)
    # Windows Installer XML 2.0 (WiX) (Microsoft)

    Web Development Tools
    # CollabNet Enterprise Edition (CollabNet)
    # Contribute 3.0 (Macromedia)
    # FlexWiki 1.8 (Microsoft Shared Source Initiative)
    # Macromedia Flex 1.5 (Macromedia)
    # NitroX 2.0 (M7 Corporation)
    # Sun Java Studio Creator (Sun Microsystems)
    # Tomcat 5.0 (The Apache Jakarta Project and leading Tomcat contributor JBoss)
    # Zend Studio 4.0 (Zend Technologies)

    Websites and Developer Networks
    # Agile Modeling Home Page (Ambysoft Inc.)
    # developer.* (DeveloperDotStar.com) (Daniel Read)
    # eBay Developers Program (eBay)
    # gantthead.com (gantthead.com)
    # IBM developerWorks (IBM)
    # Java.net (Sun Microsystems)
    # The O'Reilly Network (O'Reilly)
    参考资料
    15th Annual Software Development Jolt Product Excellence Awards

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RT-DETR改进策略【模型轻量化】| 替换骨干网络为 GhostNet V3 2024华为的重参数轻量化模型 本文记录的是基于 GhostNetV3 的 RT-DETR轻量化改进方法研究。GhostNetV3的轻量模块采用方法，训练时为深度可分离卷积和1×1卷积添加，推理时通过移除分支、折叠操作，能够在不增加推理成本的同时提高性能，从而实现RT-DETR的轻量化改进。 阅读详情

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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');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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