“…To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling. This progress in the field of machine learning is great news for the tech industry and humanity in general. …”

“…Natural language processing (NLP) involves the application of machine learning and other statistical techniques to derive insights from language. …”

“…This LiveLesson video covers the core principles of Artificial Intelligence and Machine Learning, including how to frame a problem in terms of Machine Learning and how Machine Learning is different than statistics. Learn about fundamental concepts including nearest neighbors, decision trees, and neural networks. …”

“…Nearly 1,500 well-organized, up-to-date definitions cover: accounting, customer service, distribution, e-business, economics, finance, forecasting, HR, industrial engineering, industrial relations, inventory management, healthcare management, Lean, logistics, maintenance engineering, management IS, marketing/sales, product development, operations research, organizational behavior/management, time management, production planning/control, purchasing, reliability, quality, service management, simulation, statistics, strategic management, systems engineering, supply chain management, theory of constraints, transportation, warehousing, and more. …”

“…Coverage includes Ruby 2.1 overview: terminology, philosophy, and basic principles Best practices for strings and regular expressions Efficiently internationalizing your code Performing calculations (including trigonometry, calculus, statistics, and time/date calculations) Working with “Rubyesque” objects such as symbols and ranges Using arrays, hashes, stacks, queues, trees, graphs, and other data structures Efficiently storing data with YAML, JSON, and SQLite3 Leveraging object-oriented and dynamic features, from multiple constructors to program inspection Building GUIs with Shoes 4, Ruby/Tk, Ruby/GTK3, QtRuby, and other toolkits Improving thread performance by understanding Ruby’s synchronization methods and avoiding its pitfalls Automating system administration with Ruby Data formats: JSON, XML, RSS, Atom, RMagick, PDF, and more Testing and debugging with RSpec, Minitest, Cucumber, byebug, and pry Measuring Ruby program performance Packaging and distributing code, and managing dependencies with Bundler Network programming: clients, time servers, POP, SMTP, IMAP, Open-URI Web applications: HTTP servers, Rails, Sinatra, HTML generation, and more Writing distributed Ruby software with drb Choosing modern development tools that maximize your productivity All source code for this book may be downloaded at www.rubyhacker.com. informit.com/aw informit.com/ruby rubyhacker.com/therubyway therubyway.io…”

“…You'll also explore use cases for data engineering, time series analysis, statistical analysis, and machine learning, providing essential strategies for securing and optimizing your Polars workflows. …”

Tabla de Contenidos: “…9.2.3 Importance of Layout Verification and Catastrophic Failure 350 -- 9.3 Chip Package, Test PCB, and Experimental Setup 354 -- 9.3.1 Bonding Diagram and Package 354 -- 9.3.2 Test PCB 355 -- 9.4 Experimental Test Set-Up 355 -- 9.4.1 Planning the Type and Number of Instruments Needed 357 -- 9.4.2 Connecting Lab Instruments 357 -- 9.4.3 Measurement Set-Up Example 358 -- 9.5 ΣΔM Design Examples and Case Studies 359 -- 9.5.1 Programmable-gain ΣΔMs for High Dynamic Range Sensor Interfaces 360 -- 9.5.1.1 Main Design Criteria and Performance Limitations 361 -- 9.5.1.2 SC Realization with Programmable Gain and Double Sampling 362 -- 9.5.1.3 Influence of Chopper Frequency on Flicker Noise 362 -- 9.5.2 Reconfigurable SC-ΣΔMs for Multi-standard Direct Conversion Receivers 364 -- 9.5.2.1 Power-scaling Circuit Techniques 367 -- 9.5.2.2 Experimental Results 368 -- 9.5.3 Using Widely-programmable Gm-LC BP-ΣΔMs for RF Digitizers 368 -- 9.5.3.1 Application Scenario 371 -- 9.5.3.2 Gm-LC BP-ΣΔM High-level Sizing 371 -- 9.5.3.3 BP CT-ΣΔM Loop-Filter Reconfiguration Techniques 375 -- 9.5.3.4 Embedded 4-bit Quantizer with Calibration 378 -- 9.5.3.5 Biasing, Digital Control Programmability and Testability 382 -- 9.6 Summary 385 -- References 386 -- 10 Frontiers, Trends and Challenges: Towards Next-generation 𝚺𝚫 Modulators 389 -- 10.1 State-of-the-Art ADCs: Nyquist-rate versus ΣΔ Converters 390 -- 10.1.1 Conversion Energy 391 -- 10.1.2 Figures of Merit 392 -- 10.2 Comparison of Different Categories of ΣΔ ADCs 393 -- 10.2.1 Aperture Plot of ΣΔMs 406 -- 10.2.2 Energy Plot of ΣΔMs 407 -- 10.3 Empirical and Statistical Analysis of State-of-the-Art ΣΔMs 408 -- 10.3.1 SC versus CT ΣΔMs 408 -- 10.3.2 Technology used in State-of-the-Art ΣΔMs 410 -- 10.3.3 Single-Loop versus Cascade ΣΔMs 410 -- 10.3.4 Single-bit versus Multi-bit ΣΔMs 411.…”

“…We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. …”

“…Se abre así el lugar para la instauración, en alianza inequívoca con ese discurso, de la clínica del trastorno, atada a las diferentes versiones del DSM (Diagnostic and Statistical Manual of Mental Disorders), donde las TCC (Terapias Cognitivo Comportamentales) hacen su jugada de abolición del sujeto, bajo la rúbrica de la programación: todo lo que no es programable, pasa de este modo a la categoría del trastorno. …”

“…He explains data transformations; presents expert techniques in JavaScript, Ruby, and SQL; and illuminates key concepts associated with both descriptive statistics and geospatial data. Throughout, everything is aimed at one goal: to help you cut through the clutter and let your data tell all it can. …”

“…We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. …”

“…To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics…”

“…The book wraps up the analytics portion with the application of statistical analysis, complex event processing, and deep learning models. …”

“…Along with imparting crucial statistical information to the player, the UI is also the window through which the player engages with the world established by the game. …”

“…Prior experience with Python and statistical knowledge is essential to make the most out of this book. …”

“…What you will learn Understand the basic and advanced skills of Tableau Desktop Implement best practices of visualization, dashboard, and storytelling Learn advanced analytics with the use of build in statistics Deploy the multi-node server on Linux and Windows Use Tableau with big data sources such as Hadoop, Athena, and Spectrum Cover Tableau built-in functions for forecasting using R packages Combine, shape, and clean data for analysis using Tableau Prep Extend Tableau's functionalities with REST API and R/Python Who this book is for Tableau 2019.x Cookbook is for data analysts, data engineers, BI developers, and users who are looking for quick solutions to common and not-so-common problems faced while using Tableau products. …”

“…By retroactively expanding research data to the pre-statistical era, the method enables long-duration comparison of different periods and areas. …”

“…What You Will Learn Install and set up Spark in your cluster Prototype distributed applications with Spark's interactive shell Perform data wrangling using the new DataFrame APIs Get to know the different ways to interact with Spark's distributed representation of data (RDDs) Query Spark with a SQL-like query syntax See how Spark works with big data Implement machine learning systems with highly scalable algorithms Use R, the popular statistical language, to work with Spark Apply interesting graph algorithms and graph processing with GraphX In Detail When people want a way to process big data at speed, Spark is invariably the solution. …”

“…What You Will Learn Read, clean, transform, and store your data usng Pandas and OpenRefine Understand your data and explore the relationships between variables using Pandas and D3.js Explore a variety of techniques to classify and cluster outbound marketing campaign calls data of a bank using Pandas, mlpy, NumPy, and Statsmodels Reduce the dimensionality of your dataset and extract the most important features with pandas, NumPy, and mlpy Predict the output of a power plant with regression models and forecast water flow of American rivers with time series methods using pandas, NumPy, Statsmodels, and scikit-learn Explore social interactions and identify fraudulent activities with graph theory concepts using NetworkX and Gephi Scrape Internet web pages using urlib and BeautifulSoup and get to know natural language processing techniques to classify movies ratings using NLTK Study simulation techniques in an example of a gas station with agent-based modeling In Detail Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. …”