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PyMC: a modern, and comprehensive probabilistic programming framework in Python
PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural syntax statisticians use to describe models. PyMC leverages the symbolic computation library PyTensor, allowing it to be compiled into a variety of computational backends, such as C, JAX, and Numba, which in turn offer access to different computational architectures including CPU, GPU, and TPU. Being a general modeling framework, PyMC supports a variety of models including generalized hierarchical linear regression and classification, time series, ordinary differential equations (ODEs), and non-parametric models such as Gaussian processes (GPs). We demonstrate PyMC’s versatility and ease of use with examples spanning a range of common statistical models. Additionally, we discuss the positive role of PyMC in the development of the open-source ecosystem for probabilistic programming.
Journal Article
Project reliability engineering : pro skills for next level maker projects
Turn your projects from a weekend hack to a long-living creation! Loosely drawing from the field known in large software companies as and Site reliability engineering (SRE), this book distills from these disciplines and addresses issues that matter to makers: keeping projects up and running, and providing means to control, monitor, and troubleshoot them. Most examples use the Raspberry, Pi, but the techniques discussed apply to other platforms as well. This book is all about breadth, and in the spirit of making, it visits different technologies as needed. However, the big goal in this book is to create a shift in the reader's mindset, where weekend hacks are pushed to the next level and are treated as products to be deployed. In that regard, this book can be a stepping stone for hobbyist makers into developing a broader, professional skill set. First, the book describes techniques for creating web-browser based dashboards for projects. These allow project creators to monitor, control, and troubleshoot their projects in real-time. \"Project reliability engineering\" discusses various aspects of the process of creating a web dashboard, such as network communication protocols, multithreading, and web design, and data visualization.
Book
JAVA Basics Using ChatGPT/GPT-4
Encourages readers to compare and contrast hand-written code with ChatGPT-generated code.This approach fosters discussions on code efficiency, readability, and maintainability, enhancing understanding of programming paradigms and techniques.
eBook
Windows PowerShell in action
Windows PowerShell in Action, Third Edition is the definitive guide to PowerShell, now revised to cover PowerShell 6. Written by language designer Bruce Payette and MVP Richard Siddaway, this rich book offers a crystal-clear introduction to the language along with its essential everyday use cases. Beyond the basics, you'll find detailed examples on deep topics like performance, module architecture, and parallel execution. In 2006, Windows PowerShell reinvented the way administrators and developers interact with Windows. Today, PowerShell is required knowledge for Windows admins and devs. This powerful, dynamic language provides command-line control of the Windows OS and most Windows servers, such as Exchange and SCCM. And because it's a first-class .NET language, you can build amazing shell scripts and tools without reaching for VB or C#.
Book
SymPy: symbolic computing in Python
SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
Journal Article
Essential PowerShell for Office 365 : managing and automating skills for improved productivity
\"Take your Office 365 skills to the next level. Master PowerShell for Office 365 to stay competitive in today's world of highly sought after cloud management skills. With expert guidance, IT pros will learn how to leverage the muscle of PowerShell to automate many advanced administrative tasks not otherwise accessible in the Office 365 Admin Center. You will discover how to unlock configuration options and automate tasks in order to free up valuable time and resources.This book is your companion to administering Office 365 with PowerShell. You will learn time-saving techniques such as how to streamline administrative tasks, and how to manage users, licenses, and Office 365 services. Expert and MVP Vlad Catrinescu introduces each chapter with an overview and basic fundamentals, such as how to connect to your required service in Office 365, so that you have a solid foundation for success. Benefit from learning the theory behind PowerShell for Office 365 and put your knowledge to practice with numerous hands-on code examples.What You'll LearnManage users in bulkExport data such as user lists and groupsCreate and manage Office 365 groupsManage Exchange online distribution lists, mailboxes, and contactsConfigure Skype for Business settingsPerform compliance searches directly from PowerShellWho This Book Is For Any IT pro who needs to manage Office 365 or one of its services such as Exchange, SharePoint, or Skype for Business. Readers should have a basic knowledge of PowerShell and the Office 365 service they want to manage.\"--Publisher's description.
Book
Dropout vs. batch normalization: an empirical study of their impact to deep learning
Overfitting and long training time are two fundamental challenges in multilayered neural network learning and deep learning in particular. Dropout and batch normalization are two well-recognized approaches to tackle these challenges. While both approaches share overlapping design principles, numerous research results have shown that they have unique strengths to improve deep learning. Many tools simplify these two approaches as a simple function call, allowing flexible stacking to form deep learning architectures. Although their usage guidelines are available, unfortunately no well-defined set of rules or comprehensive studies to investigate them concerning data input, network configurations, learning efficiency, and accuracy. It is not clear when users should consider using dropout and/or batch normalization, and how they should be combined (or used alternatively) to achieve optimized deep learning outcomes. In this paper we conduct an empirical study to investigate the effect of dropout and batch normalization on training deep learning models. We use multilayered dense neural networks and convolutional neural networks (CNN) as the deep learning models, and mix dropout and batch normalization to design different architectures and subsequently observe their performance in terms of training and test CPU time, number of parameters in the model (as a proxy for model size), and classification accuracy. The interplay between network structures, dropout, and batch normalization, allow us to conclude when and how dropout and batch normalization should be considered in deep learning. The empirical study quantified the increase in training time when dropout and batch normalization are used, as well as the increase in prediction time (important for constrained environments, such as smartphones and low-powered IoT devices). It showed that a non-adaptive optimizer (e.g. SGD) can outperform adaptive optimizers, but only at the cost of a significant amount of training times to perform hyperparameter tuning, while an adaptive optimizer (e.g. RMSProp) performs well without much tuning. Finally, it showed that dropout and batch normalization should be used in CNNs only with caution and experimentation (when in doubt and short on time to experiment, use only batch normalization).
Journal Article
Learning React : functional web development with React and Redux /
If you want to learn how to build efficient user interfaces with React, this is your book. Authors Alex Banks and Eve Porcello show you how to create UIs with this small JavaScript library that can deftly display data changes on large-scale, data-driven websites without page reloads. Along the way, you'll learn how to work with functional programming and the latest ECMAScript features.
Book
Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning
Optimization techniques, particularly meta-heuristic algorithms, are highly effective in optimizing and enhancing efficiency across diverse models and systems, renowned for their ability to attain optimal or near-optimal solutions within a reasonable timeframe. In this work, the Puma Optimizer (PO) is proposed as a new optimization algorithm inspired from the intelligence and life of Pumas in. In this algorithm, unique and powerful mechanisms have been proposed in each phase of exploration and exploitation, which has increased the algorithm’s performance against all kinds of optimization problems. In addition, a new type of intelligent mechanism, which is a type of hyper-heuristic for phase change, is presented. Using this mechanism, the PO algorithm can perform a phase change operation during the optimization operation and balance both phases. Each phase is automatically adjusted to the nature of the problem. To evaluate the proposed algorithm, 23 standard functions and CEC2019 functions were used and compared with different types of optimization algorithms. Moreover, using the statistical test T-test and the execution time to solve the problem have been discussed. Finally, it has been tested using four machine learning and data mining problems, and the results obtained from all the analysis signifies the excellent performance of this algorithm against all kinds of problems compared to other optimizers. This algorithm has performed better than the compared algorithms in 27 benchmarks out of 33 benchmarks and has obtained better results in solving the clustering problem in 7 data sets out of 10 data sets. Furthermore, the results obtained in the problems of community detection and feature selection and MLP were superior. The source codes of the PO algorithm are publicly available at
https://www.mathworks.com/matlabcentral/fileexchange/157231-puma-optimizer-po
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Journal Article