Leverage SharePoint Online Modern Experience to create beautiful, dynamic and mobile-ready sites and pages

Description
Lots 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.

Audience
This 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 Features

• Learn 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 2019
• Effectively use Modern list and Libraries in SharePoint Online or SharePoint 2019
• Learn about various Modern SharePoint web parts
• Create attractive and responsive portals in SharePoint Online or SharePoint 2019

Table of Contents

1. Data Science Fundamentals
2. Installing Software and Setting up
3. Lists and Dictionaries
4. Function and Packages
5. NumPy Foundation
6. Pandas and Dataframe
7. Interacting with Databases
8. Thinking Statistically in Data Science
9. How to import data in Python?
10. Cleaning of imported data
11. Data Visualization
12. Data Pre-processing
13. Supervised Machine Learning
14. Unsupervised Machine Learning
15. Handling Time-Series Data
16. Time-Series Methods
17. Case Study 1
18. Case Study 2
19. Case Study 3
20. Case Study 4

About the Author
Bijaya 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.

 

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Spis treści

Deep Learning with C#, .Net and Kelp.Net eBook -- 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