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Get to grips with the basics of JupyterLab and its web interface with the help of this quick start guide Key Features Manage JupyterLab kernels, code consoles, and terminals, and share your work over the cloud Organize your data solutions within JupyterLab Install and configure extensions on JupyterLab using easy-to-follow examples Book Description JupyterLab is a web-based interface and the natural evolution of Jupyter Notebook. This guide will take you through the core commands and functionalities of JupyterLab and help you enhance your JupyterLab productivity. Starting with the installation of JupyterLab, this book will give you an overview of its features and the variety of problems it solves. You'll see how you can work with external files inside the platform, and understand how to use the code console and terminal. This will help you have distinct control over the scripts you work with. As you progress, you'll get to grips with the extensions available in JupyterLab, and gain insights into adding extensions to introduce new functionality in the interface. This book also covers widget operations within your document, different design patterns in JupyterLab, and the various methods for exchanging Notebooks. Additionally, you'll explore how to host JupyterLab Notebooks on GitHub. By the end of this Jupyter book, you'll have become well-versed with all the components of JupyterLab and be able to use it collaboratively within your data science teams. What you will learn Install JupyterLab and work with Jupyter Notebooks Host JupyterLab Notebooks on GitHub and access GitHub resources in your Notebooks Explore different methods for exchanging Notebooks Discover ways in which multiple users can access the same Notebook Publish your Notebooks with nbviewer and convert them into different formats Attach and operate widgets within your Notebooks using a JupyterLab document Use JupyterLab to work collaboratively with multiple data scientists in your teams Who this book is for This book is for data scientists and data analysts who are new to JupyterLab as well as for existing Jupyter users who want to get acquainted with its impressive features. Although not necessary, basic knowledge of Python will be helpful.Lindsay Richman is a Product Manager who has worked in product, analytics and consulting within a variety of industries. She is passionate about the Jupyter project, and JupyterLab's role in democratizing scientific computing. She wrote Ch. 5 for this book; proceeds from her chapter will be donated to NumFocus. Melissa Ferrari completed her Ph.D. in physics at New York University. Jupyter has been a pivotal tool in her research as a method for exploratory data analysis (especially with interactive widgets), prototyping data analysis pipelines, interactive modeling, and adhering to scientific reproducibility and transparency standards. Joseph Oladokun is a Data Scientist at eHealth Africa in Nigeria, where he has an in-depth understanding of advanced techniques and tools needed to generate insights from data using the best practices with his experience in data analytics, engineering, and machine learning. Joseph is also a leader and mentor for various data science communities in Africa, and he is the founder of Data Science in Africa, an organization that uses the information to empower data scientists in Africa. He's also the co-lead of Africa R Users Group. Beyond his profession, Joseph is a leader who is very passionate about sharing information and ideas with others. Wesley Banfield is an R&D Geologist with a passion for digital innovation. He has worked in tech companies leveraging his software development skills and geological background to provide novel solutions. Throughout his career, his go-to too

Create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with JupyterKey FeaturesLearn how to use Jupyter 5.x features such as cell tagging and attractive table stylesLeverage big data tools and datasets with different Python packagesExplore multiple-user Jupyter Notebook serversBook DescriptionThe Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you've explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization. What you will learnInstall and run the Jupyter Notebook system on your machineImplement programming languages such as R, Python, Julia, and JavaScript with the Jupyter NotebookUse interactive widgets to manipulate and visualize data in real timeStart sharing your Notebook with colleaguesInvite your colleagues to work with you on the same NotebookOrganize your Notebook using Jupyter namespacesAccess big data in Jupyter for dealing with large datasets using SparkWho this book is forLearning Jupyter 5 is for developers, data scientists, machine learning users, and anyone working on data analysis or data science projects across different teams. Data science professionals will also find this book useful for performing technical and scientific computing collaboratively.

Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in RKey Features[•] Predict and use a probabilistic graphical models (PGM) as an expert system[•] Comprehend how your computer can learn Bayesian modeling to solve real-world problems[•] Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R packageBook DescriptionProbabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models. We'll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we'll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you'll see the advantage of going probabilistic when you want to do prediction. Next, you'll master using R packages and implementing its techniques. Finally, you'll be presented with machine learning applications that have a direct impact in many fields. Here, we'll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems. What you will learn[•] Understand the concepts of PGM and which type of PGM to use for which problem[•] Tune the model's parameters and explore new models automatically[•] Understand the basic principles of Bayesian models, from simple to advanced[•] Transform the old linear regression model into a powerful probabilistic model[•] Use standard industry models but with the power of PGM[•] Understand the advanced models used throughout today s industry[•] See how to compute posterior distribution with exact and approximate inference algorithmsWho this book is forThis book is for anyone who has to deal with lots of data and draw conclusions from it, especially when the data is noisy or uncertain. Data scientists, machine learning enthusiasts, engineers, and those who curious about the latest advances in machine learning will find PGM interesting.