“…Por otro lado, arroja datos sobre el análisis cuantitativo y cualitativo de los signos de puntuación incluidos en los comentarios del corpus seleccionado; este corpus ha sido extraído de seis redes sociales diferentes: Facebook, Instagram, TikTok, Twitter, WhatsApp y YouTube. …”

“…Die alterstypische Kommunikation hat sich stark in digitale Social Networks verlagert (facebook, tumblr. etc.). Informationen und Interpretationen werden sehr vorrangig online recherchiert, vor allem in der Wikipedia. …”

Tabla de Contenidos: “…-- Moving average and autoregression -- Model selection and order -- Exponential smoothing -- ARCH and GARCH -- Vector autoregression -- Python libraries -- Statsmodels -- Python practice -- Requirements -- Modeling in Python -- Summary -- Chapter 6: Unsupervised Methods for Time-Series -- Unsupervised methods for time-series -- Anomaly detection -- Microsoft -- Google -- Amazon -- Facebook -- Twitter -- Implementations -- Change point detection -- Clustering -- Python practice -- Requirements -- Anomaly detection -- Change point detection -- Summary -- Chapter 7: Machine Learning Models for Time-Series -- More machine learning methods for time series -- Validation -- K-nearest neighbors with dynamic time warping -- Silverkite -- Gradient boosting -- Python exercise -- Virtual environments -- K-nearest neighbors with dynamic time warping in Python -- Silverkite -- Gradient boosting -- Ensembles with Kats -- Summary -- Chapter 8: Online Learning for Time-Series -- Online learning for time series -- Online algorithms -- Drift -- Drift detection methods -- Adaptive learning methods -- Python practice -- Drift detection -- Regression -- Model selection -- Summary -- Chapter 9: Probabilistic Models for Time-Series -- Probabilistic Models for Time-Series -- Prophet -- Markov Models -- Fuzzy Modeling -- Bayesian Structural Time-Series Models -- Python Exercise -- Prophet -- Markov Switching Model -- Fuzzy Time-Series -- Bayesian Structural Time-Series Modeling -- Summary -- Chapter 10: Deep Learning for Time-Series -- Introduction to deep learning -- Deep learning for time series -- Autoencoders -- InceptionTime -- DeepAR -- N-BEATS -- Recurrent neural networks -- ConvNets -- Transformer architectures -- Informer -- Python practice -- Fully connected network -- Recurrent neural network…”

“…Zuckerberg, Dorsey, Cheskey, Kalanick, Evan Spiegel—captains of Facebook, Twitter, Airbnb, Snapchat—revolutionary founders who have created massively valuable companies impacting billions. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contributors -- Table of Contents -- Preface -- Part 1: Getting Started with Prophet -- Chapter 1: The History and Development of Time Series Forecasting -- Understanding time series forecasting -- The problem with dependent data -- Moving averages and exponential smoothing -- ARIMA -- ARCH/GARCH -- Neural networks -- Prophet -- Recent developments -- NeuralProphet -- Google's "robust time series forecasting at scale" -- LinkedIn's Silverkite/Greykite -- Uber's Orbit -- Summary -- Chapter 2: Getting Started with Prophet -- Technical requirements -- Installing Prophet -- Installation on macOS -- Installation on Windows -- Installation on Linux -- Building a simple model in Prophet -- Interpreting the forecast DataFrame -- Understanding components plots -- Summary -- Chapter 3: How Prophet Works -- Technical requirements -- Facebook's motivation for building Prophet -- Analyst-in-the-loop forecasting -- The math behind Prophet -- Linear growth -- Logistic growth -- Seasonality -- Holidays -- Summary -- Part 2: Seasonality, Tuning, and Advanced Features -- Chapter 4: Handling Non-Daily Data -- Technical requirements -- Using monthly data -- Using sub-daily data -- Using data with regular gaps -- Summary -- Chapter 5: Working with Seasonality -- Technical requirements -- Understanding additive versus multiplicative seasonality -- Controlling seasonality with the Fourier order -- Adding custom seasonalities -- Adding conditional seasonalities -- Regularizing seasonality -- Global seasonality regularization -- Local seasonality regularization -- Summary -- Chapter 6: Forecasting Holiday Effects -- Technical requirements -- Adding default country holidays -- Adding default state/province holidays -- Creating custom holidays -- Creating multi-day holidays -- Regularizing holidays -- Global holiday regularization…”

“…In an age of smartphones, Facebook, and YouTube, privacy may seem to be a norm of the past. …”

“…This isn't a book about social media and the inexorable rise of Facebook and Twitter. Nor is it a book about CSR or business doing good. …”

“…API-Design weiterhin ein Trendthema- GraphQL-Implementierung für JavaScript auf GitHub 16.000 Sterne- Von Facebook entwickelt, durch Foundation unabhängig, Open Source- 1. deutsches Buch zum Thema (Stand Mai 2020)- Zielgruppe Softwareentwickler mit und ohne API-Kenntnisse- Beispiele in Java und JavaScript…”

“…With a fraction of the resources of Google, Amazon, or Facebook, they have a similar impact on the future of the Internet. …”

“…Engaging with social media such as Facebook, Twitter, blogs and social news sites is now a key part of global business communications. …”

“…With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. …”

Tabla de Contenidos: “…Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- List of illustrations -- Acknowledgements -- 1 Introduction -- 2 A new constitutional moment -- Constitutional equilibrium -- Digital revolution -- Power over digital lives -- Orwellian nightmares -- New rulers -- Digital rights -- Enhanced freedoms -- Higher risks -- Constitutional change -- 3 Constitutional counteractions -- Targeted transformations -- Four examples -- Right to digital information -- Due process online -- Right to Internet access -- Data protection -- Uneven elaboration -- 4 Norms beyond the state -- A functional approach -- Constitutions and declarations -- Constitutional charters -- Limits of constitutions -- Internet bills of rights -- Public and private legislators -- Advantages of ordinary law -- Some examples -- Lex digitalis -- Lex Facebook -- Code as a constitution -- Courts and arbitrators -- National and supranational courts -- Private arbiters -- ICANN's arbitrators -- 5 The constitutionalisation of the digital society -- Constitutional pluralism -- The age of globalisation -- Multilevel theory -- Double reflexivity -- A single phenomenon -- Plurality and fragmentation -- Progressive translation -- Societal input -- 6 Digital constitutionalism -- Constitutionalism vs constitutionalisation -- The values of constitutionalism -- Constitutionalism in the digital age -- A new constitutionalism? …”

“…Cuna de las tecnologías digitales (Google, Apple, Facebook, Uber, Netflix), Silicon Valley encarna el triunfo industrial más insolente de nuestra época. …”

“…How do today’s most successful tech companies—Amazon, Google, Facebook, Netflix, Tesla—design, develop, and deploy the products that have earned the love of literally billions of people around the world? …”

Tabla de Contenidos: “…-- 6 DIVERTIDAS HERRAMIENTAS DE MEDICIÓN -- 6.1 INTRODUCCIÓN -- 6.2 GOOGLE ANALYTICS -- 6.3 GOOGLE SEARCH -- 6.4 ESTADÍSTICAS DE FACEBOOK -- 6.5 ¿POR QUÉ MIS VISITANTES ABANDONAN MI PÁGINA WEB?…”

Tabla de Contenidos:

Tabla de Contenidos: “…The Hot-Stove Effect: Learning from FailureThe Expectations Effect: Hope Versus Reality; Innovation's Gain-Loss Function: Type 1 and 2 Errors; Case Histories; Gambling on an Embryonic Market: Toyota's Prius; Initial Stage: Striving for Kakushin (''Rapid Innovation'')-Aim High; Experimentation-Undaunted by Failure; Slow Takeoff-A Testament of Toyota's Faith and Patience; Eventual Success; Gambling on Growth: Amazon.com; Gambling on Vision: Facebook; Hatch of an Idea in a Dorm Room; Bet on Silicon Valley; Risk-Loving Hacker Culture; News Feed Crisis; To Sell or Not to Sell…”

Tabla de Contenidos: “…Frontmatter -- Preface -- TABLE OF CONTENT -- Semantic Preferences of Indonesian Words Hitam and Putih -- The Stylistic Analysis of Indonesian Online News in The Five Websites -- Proto Austronesian Language Sound Changes To Mandailing Language -- The Use of Verb + Noun Collocations by English Learners of Indonesian -- An Examination of Islamic Verbal and Non-Verbal Communication in Retail Stores -- Proto Malayic Reflection at Isolect of Malay Jambi Seberang and Malay Sabak at Jambi Province -- Otographis Approach Toward Language Resilience Study on Japanese LanguageLexicons in Indonesia Historical Documents -- The Cultural Values in The Culinary Ecolexicon of the Asahan Malay Society -- Bioacoustics Analysis With Speech Analyzer Spectogram As A Testing Method For Speech Ability Improvement of Dysarthria Patient -- Speech, Writing Disorder and Therapy of Dyslexic Patient -- The Variations of Speech in Opening and Closing TV Program Uttered by MTV Singapore VJ -- Delivering The Message of Local Spirit "Bakureh" Through The Language of Dance -- Contribution of Biological Knowledge in Understanding the Ulterior Teaching of Life within the Minangkabau Poems and Proverbs -- Social Practice In Facebook: Critical Discourse Analysis In The Process of Text Production -- Pakpak Dairi Language: Typology Study -- Speech Act of Advice toward the Victims of Domestic Violence in West Sumatera -- A Fallacy Analysis on Political Statements in the Transcript of the First Indonesia Presidential Debate 2019 -- Are the Phonological Disorders of Down Syndrome Children the Same? …”