Deep Learning with C#, .Net and Kelp.Net Siemianowice Śląskie

Leverage SharePoint Online Modern Experience to create beautiful, dynamic and mobile-ready sites and pages DescriptionLots of small, medium and large organizations or enterprises are using Office 365 for their business. And Microsoft is also investing heavily on Office 365 and providing lots of new features in Office 365 and other services in Office 365 like Office application or SharePoint Online, Yammer, Teams, Flow or PowerApps, etc. SharePoint is one of the popular portal technologies and web-based business collaboration and document management system. With Office 365 subscription, organizations can use SharePoint Online. Microsoft has announced the Modern features in SharePoint for a long time. Modern Experience is the future of SharePoint Online and on-premises also. This book is a comprehensive guide that lets you explore the Modern features in SharePoint Online or SharePoint Server 2019. In the book, I have covered details on Modern Team sites, communication sites, how you can customize the team sites according to your business requirement. You will also get hands-on Experience on how you can customize Modern site pages. I have also explained in detail various new features of Modern list and document libraries in SharePoint. This book also contains a few SharePoint portal examples, you will get in-depth knowledge on how to design team sites with various useful web parts. Few Organizations are still using SharePoint On-premises versions like SharePoint server 2019. I have also explained the Modern Experience in SharePoint 2019. Always it is better to know also, what are the things which are not possible in SharePoint Modern Experience, based on which you can check the impact, before moving to the SharePoint Online Modern Experience. AudienceThis book is for the site owners, power users or administrators who want to design attractive pages for SharePoint Modern team sites or publishing sites. Though the book is intended for SharePoint developer knowledge, but a little understanding of SharePoint is required. We have provided detailed steps with proper screenshots for references. This book is also for the developers who are trying to build pages for Modern SharePoint team sites or publishing site in SharePoint Online or SharePoint server 2019.What you will Learn In this book, you will learn what are Modern Experiences in SharePoint. How we can handle at the organizational level. What are the things which are not possible in SharePoint Online Modern Experience. Various new features of SharePoint Online Modern list and document libraries.You will also learn various web parts and how we can use those web parts while designing pages for your sites. Various examples of SharePoint Modern portal designs. How we can create and customize Modern site pages. How we can also start with SharePoint Server 2019 and use various Modern web parts in SharePoint 2019 sites.Key FeaturesLearn how to use SharePoint Online Modern Experience (Modern UI)Create a Modern team site and communication site for your organization in SharePoint Online or SharePoint Server 2019Effectively use Modern list and Libraries in SharePoint Online or SharePoint 2019Learn about various Modern SharePoint web parts Create attractive and responsive portals in SharePoint Online or SharePoint 2019Table of Contents Data Science FundamentalsInstalling Software and Setting upLists and DictionariesFunction and PackagesNumPy FoundationPandas and DataframeInteracting with DatabasesThinking Statistically in Data ScienceHow to import data in Python?Cleaning of imported dataData VisualizationData Pre-processingSupervised Machine LearningUnsupervised Machine LearningHandling Time-Series DataTime-Series Methods Case Study 1Case Study 2Case Study 3Case Study 4 About the AuthorBijaya is a Microsoft MVP (Office Servers & Services) and having more than 11 years of experience in Microsoft Technologies specialized in SharePoint. He is Co-founder of TSInfo Technologies, a SharePoint consulting, training & development company in Bangalore, India. He has been a technology writer for many years and writes many SharePoint articles on his websites SharePointSky.com and EnjoySharePoint.com. Bijaya is a passionate individual who loves public speaking, blogging and training others to use Microsoft products. Before co-founding TSInfo Technologies, he was working with small and large organizations in various SharePoint On-premises as well as SharePoint Online office 365 & various related technologies. Bijaya also likes to publish SharePoint videos on his EnjoySharePoint YouTube Channel. Spis treści: Cover Deep Learning with C#, .NET and Kelp.NET Copyright About the Author Reviewer Preface Acknowledgement Errata Table of Contents 1. Take This ___ and ___ It Objectives of this book Neural network overview Machine learning overview Deep learning overview Complexity Machine and deep learning differences Summary 2. Machine Learning/Deep Learning Terms and Concepts Overview Neuron/Perceptron Multi-Layer Perceptron (MLP) Features Weights Bias Activation Function Sigmoid ReLU (Rectified Linear Units) Softmax Neural network Input/Output/Hidden Layers Forward propagation Back propagation The No Free Lunch theorem The Curse of Dimensionality The more neurons versus more layers Cost function Gradient descent Learning rate Batches/Batch size Epochs Iterations Dropout Batch Normalization CNN (Convolutional Neural Network) Pooling Padding Recurrent neuron RNN (Recurrent Neural Network) Vanishing gradient problem Exploding gradient problem Logistic Neurons Hidden layers Types of neural networks Generalization Regularization Loss Loss over time Loss versus learning curve Supervised learning Bias-Variance Trade-off (overfitting and underfitting) Bias Variance Overfitting Is your model overfitting or underfitting? Prevention of overfitting and underfitting Amount of training data Input space dimensionality Incorrect output values Data heterogeneity Unsupervised learning Reinforcement learning Manifold learning Types of manifolds in deep learning Topological Differentiable Riemannian Principal Component Analysis (PCA) Hyperparameter training Approaches to hyperparameter tuning Grid search Random search Bayesian optimization Gradient-based optimization Evolutionary optimization Summary References 3. Deep Instrumentation Using ReflectInsight Next generation logging viewers Message log Message details Message properties Bookmarks Call Stack Message Navigation Advanced Search User-Defined Views and Filtering Auto Save/Purge rolling log files Watches Time zone formatting Router Log viewer Live viewer SDK Configuration editor Overview XML configuration Dynamic configuration Configuration editor Message type logging reference Assertions Assigned variables Attachments Audit failure and success Checkmarks Checkpoints Collections Comments Currency Data DataSet DataSetSchema DataTable DataTableSchema DataView Date/Time Debug Desktop Image Errors Exceptions Fatal Errors Generations Images Information Levels Linq queries and results Loaded assemblies Loaded processes Memory status Messages Notes Process Information Reminders Serialized Objects SQL strings Stack Traces System Information Text files Thread Information Typed collections Warning XML XML files Tracing method calls Attaching message properties To one request To all requests To a single message Watches Using custom data Output Summary 4. Kelp.Net Reference Let us be honest Downloading Kelp.Net Building the source code What is Kelp.Net? N-dimensional arrays Optimizers AdaDelta AdaGrad Adam GradientClippin g MomentumSGD RMSprop SGD Poolings MaxPooling AveragePooling FunctionStack FunctionDictionary SplitFunction SortedList SortedFunctionStack Activation Functions Activation plots ArcSinH ArcTan ELU Gaussian LeakyReLU LeakyReLUShifted LogisticFunction MaxMinusOne PolynomialApproximantSteep QuadraticSigmoid RbfGaussian ReLU ReLuTanh ScaledELU Sigmoid Sine Softmax Softplus SReLU SReLUShifted Swish Tanh Connections Convolution2D Deconvolution2D EmbedID Linear LSTM Normalization BatchNormalization Local Response Normalization Noise Dropout StochasticDepth Loss MeanSquaredError SoftmaxCrossEntropy Datasets CIFAR-10 CIFAR-100 MNIST Street View House Numbers (SVHN) Summary References 5. Model Testing and Training Accuracy Timing Common stacks Summary 6. Loading and Saving Models Loading models Saving models Model size Summary 7. Sample Deep Learning Tests A simple XOR problem Complete source code Output A penny for your thoughts A simple XOR problem (part 2) Complete source code Output Recurrent Neural Network Language Models (RNNLM) Complete source code Vocabulary Output Word prediction test Complete source code Output Decoupled Neural Interfaces using Synthetic Gradients Output MNIST accuracy tester Complete source code Output Massively Deep Network Test Complete source code Output Image prediction test Complete source code Output Function benchmarking Output MNIST (handwritten characters) learning test Complete source code Output LeakyReLu and PolynomialApproximantSteep Combination Network Complete source code Output FunctionStack navigation tests Complete source code Output Learning Rate Hyperparameter tester Complete source code Output Model scoring Complete source code Output Summary 8. Creating Your Own Deep Learning Tests Example Implementing the Run function Create a FunctionStack with your functions Set the optimizer Make your predictions Save the model Loading models Summary Thank You Appendix A Evaluation metrics Metrics terminology Confusion matrix Appendix B OpenCL OpenCL hierarchy O autorze: Matt R. Cole od 30 lat programuje dla systemu Windows — biegle posługuje się językami: C, C++, C# oraz platformą .NET. Napisał system generowania mowy oraz system VOIP dla NASA, którego używano na promach kosmicznych i stacji kosmicznej. Przygotował pierwszy framework mikrousług klasy enterprise (napisany w całości w C# i .NET), wykorzystywany przez jeden z głównych funduszy hedgingowych. Napisał też framework sztucznej inteligencji, w którym zintegrowane zostały neurony lustrzane i kanoniczne.