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Programmierung Programmierung Administration • Links • Vorgehensmodelle • NuGet • Transformations • MSBuild • Raspberry • Cheatsheets • Git • Mathematica C♯ Program Flow Multithreading and asynchronous processing • Multithread management • Program flow • Events and callbacks • Exception handling Types Type creation • Type consumentation • Encapsulation • Class hierarchies • Reflection • Object life cycle • String manipulation Debugging and Security Input validation • Encryption • Assembly Management • Debugging • Diagnostics Data access I/O operations • Data consumtion • LINQ • Data serialization • Collections Theory Functional • Lambda Calculus • Category Theory F♯ Basics Data Structures • Classes • Generics • Units of Measure • Exception Handling • Pipes • Recursion • Pattern Matching • Lazy Evaluation • Asynchronous and Parallel Programming • Number Representations • Arithmetic Operators • Conversion Functions • Queries Quantitative Finance Math.NET • Statistics • Root Finding Algorithmns • Data Visualisation • Option Pricing • Volatility • Order Types and Market Data • Volatility Trading Python Classes • Loops • Anonymous Types • Lambda Expressions • Queries (LINQ) • Iterators (yield) PHP   T-SQL / SQL Server T-SQL Fundamantals Select • Programming • Pitfalls • Query from Multiple Tables • Groups and Summaries • Advanced Select • Aggregation and Windowing • Insert, Update, Delete • Strings • Date and Time • Numbers • Transactions, Locking, Blocking, Deadlock • Tables • Views • Large Tables and Databases • Indexes • Stored Procedures • User-Defined Functions and Types • Triggers • Error Handling • Query Performance and Tuning • Hints • Index Tuning and Statistics • XML • Files, Filegroups, Integrity • Backup • Recovery • Principals and Users • Securables, Permissions, Auditing • Objects and Dependencies SQL Server Performance Tuning Memory Performance Analysis • Disk Performance Analysis • CPU Performance Analysis • Baseline Creation • Query Performance Metrics • Query Performance Analysis • Index Architecture and Behavior • Index Analysis • Database Engine Tuning Advisor • Key Lookups • Statistics, Data Distributuon, and Cardinality • Index Fragmentation • Execution Plan Generation • Execution Plan Cache Behavior • Parameter Sniffing • Query Recompilation • Query Design Analysis • Reduce Query Resource Use • Blocking and Blocked Processes • Causes and Solutions for Deadlocks • Row-By-Row Processing • Memory-Optimized OLTP Tables and Procedures • Database Performance Testing • Database Workload Optimization • SQL Server Optimization Checklist Service Broker Message • Contract • Queue • Service • .NET Integration • Route • Security   Datatypes • Unit Testing • Dependency Injection • Partitioning • .NET General LINQ • Reactive Extensions • Entity Framework • OData • Windows Workflow Foundation • Threading • Logging • Unit Testing • Dependency Injection • Documentation • WinForms Performance Locking • Coding Style and Class Design • .NET Framework • Performance Counters • Event Tracing (ETW) • Windows Phone • Code Safety • Team ASP.NET Common OWIN • Razor • WebForms • MVC • WebAPI Core Configuration • Data Protection API • Dependency Injection • Routing Attack Vectors HTTPS • HSTS • HPKP • CSP • CSRF • Anti-XSS • CORS • Open Redirection • Click-Jacking Libraries OpenID • Azure AD Messaging WCF Basics Services • Addresses • Contracts • Hosting • Bindings • Endpoints • Metadata Exchange • Behaviour Configuration • Client-Side Programming • Architecture • Channels • Transport-Level Sessions • Reliability Advanced WCF Service Contract • Data Contract • Instance Management • Operations • Faults • Transactions • Concurrency Management • Queued Services • Security • Service Bus • Basic HTTP Auth Andere .NET Remoting • Message Queuing (MSMQ) • Protocol Buffers • ActiveMQ • Disruptor.NET • TIBCO Rendezvous • CQRS • Rebus User Interface WPF Data Binding • Resources • Transforms and Effects • 2D Drawing • 3D Drawing • Routed 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Lifecycle • Pipes • Routing and Navigation • Security • Structural Directives (ngIf, ngFor, ngSwitch) • Testing • Forms Validation Other Knockout • RequireJS • PhoneGap/Cordova • React • fantasy-land • typed-contract • parsimmon SharePoint Notes Fields • Lists • Content Types • Workflow • Business Connectivity Services • Search • Managed Metadata • AngularJS • Publishing/Approval • LINQ to SharePoint Nintex Custom Activities 70-488 • 70-489 Design and Implement Search • Implement Business Connectivity Services (BCS) • Implement Implement User Profiles and Customize Social Workload • Implement ECM and WCM • Design for Performance and Troubleshooting • Implement Managed Metadata Service (MMS) Microsoft CRM Reporting and Dashboards • SharePoint Integration • Authentication • Entity Handling • Workflows • Queries Security Forms Authentication • Membership and Role Provider • Zertifikate • OAuth • OpenID • Windows Identity Foundation Dateiformate CSV • Excel • Word • PDF • Email Azure Notes Azure Pack • Single Sign-on • Data Storage • Logging • Tracing • Service Bus • HDInsight • Orleans Infrastructure Services Virtual Machines • Virtual Machine Networking • Virtual Machine Storage • Virtual Networks 70-533  • 70-534 Active Directory • Static IP Adresses • ACLs • Network Security Groups • Availability Sets • VPN • Load Balancing • CDN • Media Services • Multi-Factor Auth. Muster Architektur Entwurfsmuster • Architekturmuster • Patterns of Enterprise Application Architecture Daten Analysemuster • Datenbank-Muster Verteilte Systeme Enterprise Integration Patterns • SOA • Big Data • Cloud • Stability & Capacity Management Organisationsmuster • Refactoring • Lizenzmuster Antimuster Antimuster • Dark Patterns • SQL Antipatterns Powershell Grundlagen Subversion (SVN) • CmdletBinding • Parameter (Sets) • Return Type • Module • Exception Handling DSC Common Information Model (CIM) • Resources • Configuration and Parameters • Local Configuration Manager (LCM) • Credentials • SMB Pull Server • HTTP Pull Server Android ROM Flashen • Unit Testing • Assistant Machine Learning Neuronale Netze Propagierungsfunktionen • Aktivitätsfunktionen • Lernmethoden • Pattern Associator • Rekurrente Netze • Kompetitive Netze • Kohonennetze • Constrain Statisfaction Netze Tensorflow Linear Regression • Logistic Regression • Softmax Regression • Hidden Markov • K-Means Clustering • Self-Organizing Map • Autoencoder • Q-policy neural network • Perceptron • Convolutional Neuronal Network • Recurrent Neuronal Network • Seq2Seq • Ranking Core Server Hyper-V • Active Directory User Groups • Active Directory Domain Services • Active Directory Federation Services • DHCP • IIS • DNS • RDP • WSUS • User Permissions • IP • SMB • DHCP • IPAM • Sonstige Bot Framework Rich Messages • Statistics open System let random = new Random() let randomNumber() = random.NextDouble() // define alias let randomData = [for i in 1..100 -> randomNumber() * 10.0] let sum = randomData |> Seq.sum let average = randomData |> Seq.average let minimum = randomData |> Seq.min let maximum = randomData |> Seq.max // calculate variance let variance (values:seq<float>) = let average = Seq.average values values |> Seq.map (fun x -> (1.0 / float(Seq.length values)) * (x - average) ** 2.0) |> Seq.sum let var = variance randomData // calculate standard derivation let standardDerivation (values:seq<float>) = sqrt(variance(values)) let standardDerivation (values:seq<float>) = let avg = Seq.average values values |> Seq.fold (fun acc x -> acc + (1.0 / float (Seq.length values)) * (x - avg) ** 2.0) 0.0 // apply function to every element and accumulate the result |> sqrt Statistics with Math.NET let dist = new Normal(0.0, 1.0) let samples = dist.Samples() |> Seq.take 1000 |> Seq.toList let statistics = new DescriptiveStatistics(samples) let maximum = statistics.Maximum let minimum = statistics.Minimum let mean = statistics.Mean let variance = statistics.Variance let standardDerivation = statistics.StandardDerivation Linear Regression open System open MathNet.Numerics open MathNet.Numerics.LinearAlgebra open MathNet.Numerics.LinearAlgebra.Double open MathNet.Numerics.Distributions ${\vec {v}}=a\,\mathbf {M} +b$ let M = DenseMatrix.ofColumsList 5 2 [ List.init 5 (fun i -> 1.0); [10.0; 20.0; 30.0; 40.0; 50.0] ] let v = DenseVector [| 8.0; 21.0; 32.0; 40.0; 49.0 |] let p = M.QR().Solve(v) let (a, b) = (p.[0], p.[1]) Polynomial Regression Input data is $y=x^{2}-3\,x+5$ let noise = Normal.WithMeanVariance(0.0, 0.5) let xData = [ -10.0 .. 0.2 .. 10.0 ] let yData = [ for x in xData do yield (x ** 2) - (3.0 * x) + noise.Sample() ] square estimation for coefficients ${\vec {c}}=\left(\mathbf {A} ^{T}\cdot \mathbf {A} \right)^{-1}\cdot \mathbf {A} ^{T}\cdot {\vec {y}}$ siehe auch: Vandermonde-Matrix let N = xData.length let order = 2 let vandermondeRow v = [for x in [0..order] do yield v ** (float x)] let vandermonde = xdata |> Seq.map vandermondeRow |> Seq.toList let A = (vandermonde |> DenseMatrix.ofRowsList N (order + 1)) let createYVector order l = [for x in [0..order] do yield l] let Y = (createYVector order ydata |> DenseMatrix.ofRowsList (order + 1)).Transpose() let coeffs = (A.Transpose() * A).LU().Solve(A.Transpose() * Y).Column(0) let calculate x = (vandermondeRow(x) |> DenseVector.ofList) * coeffs let fitxs = [(Seq.min xData) .. 0.2 .. (Seq.max xData)] let fitys = fitxs |> List.Map calculate let fits = [for x in [(Seq.min xData) .. 0.2 .. (Seq.max xData)] do yield (x, calculate x)] Chart Result open FSharp.Charting open System.Windows.Forms.DataVisualization.Charting fsi.AddPrinter(fun (ch:ChartTypes.GenericChart) -> ch.ShowChart(); "FSharpCharting") let chart = Chart.Combine [Chart.Point(List.zip xData yData); Chart.Line(fits).WithTitle("Polynomial regression")] Internetlinks Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2002, abgerufen am 6. Juli 2014 (englisch).