Mostrando 281 - 300 Resultados de 399 Para Buscar '"Ingestión"', tiempo de consulta: 0.10s Limitar resultados
  1. 281
    por Correa V., José Alberto
    Publicado 2011
    Tabla de Contenidos: “…-- AIEPI: NO ES SÓLO PARA LOS PAÍSES POBRES -- OBJETIVOS -- BENEFICIOS -- FRENTES DE TRABAJO DE LA ESTRATEGIA AIEPI -- MODELO AIEPI -- EL PROCESO DE ATENCIÓN INTEGRADA DE CASOS -- INSTRUMENTOS PARA APLICAR LA ESTRATEGIA AIEPI -- RESULTADOS -- CONCLUSIÓN -- REFERENCIAS BIBLIOGRÁFICAS -- CAPÍTULO 13: INAPETENCIA -- TERMINOLOGÍA -- GENERALIDADES -- MÉTODO DE LA PERSUASIÓN -- MÉTODO DE LA DISTRACCIÓN -- MÉTODO DEL SOBORNO -- TÓNICOS -- AMENAZAS -- MÉTODO DE LA FUERZA -- EL CASTIGO -- ALIMENTO ENTRE COMIDAS -- CLASIFICACIÓN DE LA INAPETENCIA -- ERRORES DE APRECIACIÓN -- NIÑOS QUE COMEN SUFICIENTE PERO NO LO MÁS ADECUADO NI EN LOS MOMENTOS MÁS OPORTUNOS -- NIÑOS QUE COMEN MAL POR ENFERMEDAD ORGÁNICA SUBYACENTE -- NIÑOS QUE COMEN MAL POR PROBLEMAS AFECTIVOS O EMOCIONALES -- ANOREXIA PSICÓGENA -- INAPETENCIA FISIOLÓGICA O ENFERMEDAD DE LOS DIECISÉIS MESES -- ESTIMULANTES DEL APETITO -- REFERENCIAS BIBLIOGRÁFICAS -- CAPÍTULO 14 DESNUTRICIÓN -- DEFINICIÓN -- CLASIFICACIÓN ETIOLÓGICA -- INTERFERENCIA CON LA INGESTIÓN -- ALTERACIONES EN LA DIGESTIÓN…”
    Libro electrónico
  2. 282
    por Goldsmith, Ben
    Publicado 2021
    Tabla de Contenidos: “…-- The Compendium -- 1 The Interface &amp -- Importing -- Before You Get Started -- Organizing Your Files -- Ingesting Footage -- To Rename or Not to Rename? -- The Interface -- Workspaces -- Resizing and Rearranging Panels -- Creating Custom Workspaces -- Panels -- The Project Panel -- Project Panel Options -- Bins -- Importing Files -- Importing Photoshop and Illustrator Files -- Importing RED footage -- Clip Options -- Metadata -- View and Edit Metadata -- Offline Media -- Relinking Media -- Proxies -- Creating Proxies -- Customizing Proxies -- Manually Creating Proxies -- Working with Proxies -- Productions -- Create a Production -- Working with Productions -- Team Projects -- 2 Sequences &amp…”
    Libro electrónico
  3. 283
    Publicado 2017
    Tabla de Contenidos: “…. -- How it works... -- Setting up an ingestion node -- Getting ready -- How to do it... -- How it works... -- Installing plugins in Elasticsearch -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Installing plugins manually -- Getting ready -- How to do it... -- How it works... -- Removing a plugin -- Getting ready -- How to do it...…”
    Libro electrónico
  4. 284
    Publicado 2018
    Tabla de Contenidos: “…MQTT-SN architecture and topology -- Transparent and aggregating gateways -- Gateway advertisement and discovery -- Differences between MQTT and MQTT-SN -- Constrained Application Protocol -- CoAP architecture details -- CoAP Messaging Formats -- CoAP usage example -- Other protocols -- STOMP -- AMQP -- Protocol summary and comparison -- Summary -- Chapter 10: Cloud and Fog Topologies -- Cloud services model -- NaaS -- SaaS -- PaaS -- IaaS -- Public, private, and hybrid cloud -- Private cloud -- Public cloud -- Hybrid cloud -- The OpenStack cloud architecture -- Keystone - identity and service management -- Glance - image service -- Nova compute -- Swift - Object Storage -- Neutron - Networking services -- Cinder - Block Storage -- Horizon -- Heat - orchestration (optional) -- Ceilometer - telemetry (optional) -- Constraints of cloud architectures for IoT -- Latency effect -- Fog computing -- The Hadoop philosophy for Fog computing -- Fog Computing versus Edge Computing versus cloud computing -- OpenFog Reference Architecture -- Application services -- Application support -- Node management and software backplane -- Hardware virtualization -- OpenFog node security -- Network -- Accelerators -- Compute -- Storage -- Hardware platform infrastructure -- Protocol abstraction -- Sensors, actuators, and control systems -- Amazon Greengrass and Lambda -- Fog Topologies -- Summary -- Chapter 11: Data Analytics and Machine Learning in the Cloud and in the Fog -- Basic data analytics in IoT -- Top-level cloud pipeline -- Rules engines -- Ingestion - streaming, processing, and data lakes -- Complex event processing -- Lambda architecture -- Sector use cases -- Machine learning in IoT -- Machine learning models -- Classification -- Regression -- Random forest -- Bayesian models -- Convolutional Neural Networks -- First layer and filters…”
    Libro electrónico
  5. 285
    Publicado 2024
    Tabla de Contenidos: “…Adding noise to images using diffusers -- Defining the UNet model -- Training the UNet model -- Defining the optimizer and learning schedule -- Using Hugging Face Accelerate to accelerate training -- Running the model training loop -- Generating realistic anime images using (reverse) diffusion -- Understanding text-to-image generation using diffusion -- Encoding text input into an embedding vector -- Ingesting additional text data in the (conditional) UNet model -- Using the Stable Diffusion model to generate images from text -- Summary -- Reference list -- Chapter 11: Deep Reinforcement Learning -- Reviewing RL concepts -- Types of RL algorithms -- Model-based -- Model-Free -- Discussing Q-learning -- Understanding deep Q-learning -- Using two separate DNNs -- Experience replay buffer -- Building a DQN model in PyTorch -- Initializing the main and target CNN models -- Defining the experience replay buffer -- Setting up the environment -- Defining the CNN optimization function -- Managing and running episodes -- Training the DQN model to learn Pong -- Summary -- Reference list -- Chapter 12: Model Training Optimizations -- Distributed training with PyTorch -- Training the MNIST model in a regular fashion -- Training the MNIST model in a distributed fashion -- Distributed training on GPUs with CUDA -- Automatic mixed precision training -- Regular model training on a GPU -- Mixed precision training on a GPU -- Summary -- Reference list -- Chapter 13: Operationalizing PyTorch Models into Production -- Model serving in PyTorch -- Creating a PyTorch model inference pipeline -- Saving and loading a trained model -- Building the inference pipeline -- Building a basic model server -- Writing a basic app using Flask -- Using Flask to build our model server -- Setting up model inference for Flask serving -- Building a Flask app to serve model…”
    Libro electrónico
  6. 286
    Publicado 2010
    Tabla de Contenidos: “…L'alimentation des animaux de trait -- Les deux périodes délicates de l'année -- À la fin de la saison sèche -- Au cours des travaux intensifs -- Les besoins alimentaires -- L'énergie -- Les matières azotées digestibles -- ❘◗ Les minéraux -- Les vitamines -- L'eau -- Les bovins -- Les buffles -- Les chevaux -- Les aliments disponibles -- Les fourrages et les aliments concentrés -- Les réserves fourragères -- La préparation et la conservation des fourrages -- La bonne alimentation de l'animal -- Les modalités et le rythme de distribution -- Les bovins -- Les chevaux -- La capacité d'ingestion et l'adaptation des rations -- L'état corporel -- Les ânes -- Les bovins -- Le calendrier et l'organisation de l'affouragement -- Quelques aspects spécifiques de l'alimentation des autres espèces -- Les ânes -- Les buffles -- Les yaks -- Les camélidés -- Les besoins énergétiques et azotés -- Les minéraux -- Les règles d'alimentation et d'abreuvement -- 6. …”
    Libro electrónico
  7. 287
    por World Health Organization
    Publicado 2022
    Tabla de Contenidos: “…Radiological aspects -- 9.1 Sources and health effects of radiation exposure -- 9.1.1 Radiation exposure through ingestion of drinking-water -- 9.1.2 Radiation-induced health effects through drinking-water -- 9.2 Rationale for screening levels and guidance levels -- 9.3 Monitoring and assessment for dissolved radionuclides -- 9.3.1 Screening of drinking-water supplies -- 9.3.2 Strategy for assessing drinking-water if screening levels are exceeded -- 9.3.3 Strategy for assessing drinking-water if guidance levels are exceeded -- 9.3.4 Sampling frequency…”
    Libro electrónico
  8. 288
    Publicado 2014
    Tabla de Contenidos: “…17.1 Introduction 329 -- 17.2 Carrier CDNs 331 -- 17.3 Managed CDNs 332 -- 17.4 Federated CDNs 333 -- 17.5 Licensed CDNs 335 -- 17.6 Case Study: CoBlitz 337 -- 17.7 CoBlitz Commercialization 343 -- 17.8 Implications of HTTP Adaptive Streaming 345 -- 17.9 CoBlitz Commercialization Lessons 347 -- 17.10 CDN Industry Directions 348 -- Acknowledgments 349 -- References 349 -- 18 CONTENT DELIVERY IN CHINA: A ChinaCache PERSPECTIVE 353 /Michael Talyansky, Alexei Tumarkin, Hunter Xu, and Ken Zhang -- 18.1 Introduction 353 -- 18.2 Content-Aware Network Services in China 356 -- 18.3 Directions for Future CDN Research and Trends in China 365 -- 18.4 Conclusion 366 -- References 366 -- 19 PlatonTV: A SCIENTIFIC HIGH DEFINITION CONTENT DELIVERY PLATFORM 369 /Mirosław Czyrnek, Jedrzej Jajor, Jerzy Jamrozy, Ewa Kusmierek, Cezary Mazurek, Maciej Stroinski, and Jan Weglarz -- 19.1 Introduction 369 -- 19.2 Background and Related Work 371 -- 19.3 PlatonTV Architecture 372 -- 19.4 Content Ingest 374 -- 19.5 Content Distribution and Management 376 -- 19.6 Content Delivery 379 -- 19.7 Availability and Reliability 381 -- 19.8 Visionary Thoughts for Practitioners 382 -- 19.9 Future Research Directions 383 -- 19.10 Conclusion 383 -- Acknowledgments 383 -- References 384 -- 20 CacheCast: A SINGLE-SOURCE MULTIPLE-DESTINATION CACHING MECHANISM 385 /Piotr Srebrny, Dag H.L. …”
    Libro electrónico
  9. 289
    Publicado 2022
    Tabla de Contenidos: “…Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- 1 Deep Dive Into Blockchain Technology: Characteristics, Security and Privacy Issues, Challenges, and Future Research Directions -- 1.1 Introduction -- 1.2 Blockchain Preliminaries -- 1.2.1 Functioning of Blockchain -- 1.2.2 Design of Blockchain -- 1.2.3 Blockchain Elements -- 1.3 Key Technologies of Blockchain -- 1.3.1 Distributed Ledger -- 1.3.2 Cryptography -- 1.3.3 Consensus -- 1.3.4 Smart Contracts -- 1.3.5 Benchmarks -- 1.4 Consensus Algorithms of Blockchain -- 1.4.1 Proof of Work (PoW) -- 1.4.2 Proof of Stake (PoS) -- 1.4.3 BFT-Based Consensus Algorithms -- 1.4.4 Practical Byzantine Fault Tolerance (PBFT) -- 1.4.5 Sleepy Consensus -- 1.4.6 Proof of Elapsed Time (PoET) -- 1.4.7 Proof of Authority (PoA) -- 1.4.8 Proof of Reputation (PoR) -- 1.4.9 Deputized Proof of Stake (DPoS) -- 1.4.10 SCP Design -- 1.5 Internet of Things and Blockchain -- 1.5.1 Internet of Things -- 1.5.2 IoT Blockchain -- 1.5.3 Up-to-Date Tendency in IoT Blockchain Progress -- 1.6 Applications of Blockchain in Smart City -- 1.6.1 Digital Identity -- 1.6.2 Security of Private Information -- 1.6.3 Data Storing, Energy Ingesting, Hybrid Development -- 1.6.4 Citizens Plus Government Frame -- 1.6.5 Vehicle-Oriented Blockchain Appliances in Smart Cities -- 1.6.6 Financial Applications -- 1.7 Security and Privacy Properties of Blockchain -- 1.7.1 Security and Privacy Necessities of Online Business Transaction -- 1.7.2 Secrecy of Connections and Data Privacy -- 1.8 Privacy and Security Practices Employed in Blockchain -- 1.8.1 Mixing -- 1.8.2 Anonymous Signatures -- 1.8.3 Homomorphic Encryption (HE) -- 1.8.4 Attribute-Based Encryption (ABE) -- 1.8.5 Secure Multi-Party Computation (MPC) -- 1.8.6 Non-Interactive Zero-Knowledge (NIZK)…”
    Libro electrónico
  10. 290
    Publicado 2023
    Tabla de Contenidos: “…4.2.4.9 The Need For Prescriptive Analytics in Maintenance: A Case Study -- 4.3 Big Data Analytics Methods -- 4.3.1 Defining Big Data Analytics -- 4.3.2 Defining Big Data Via the Three Vs -- 4.3.2.1 Data Volume as a Defining Attribute of Big Data -- 4.3.2.2 Data Type Variety as a Defining Attribute of Big Data -- 4.3.2.3 Data Feed Velocity as a Defining Attribute of Big Data -- 4.3.3 Text Analytics -- 4.3.4 Audio Analytics -- 4.3.5 Video Analytics -- 4.3.6 Social Media Analytics -- 4.4 Maintenance Strategies with Big Data Analytics -- 4.5 Data-Driven and Model-Driven Approaches -- 4.5.1 Data Mining and Knowledge Discovery -- 4.6 Maintenance Descriptive Analytics -- 4.7 Maintenance Diagnostic Analytics -- 4.8 Maintenance Predictive Analytics -- 4.9 Maintenance Prescriptive Analytics -- 4.10 Big Data Analytics Methods -- 4.10.1 Text Analytics -- 4.10.2 Audio Analytics -- 4.10.3 Video Analytics -- 4.10.4 Social Media Analytics -- 4.11 Big Data Management and Governance -- 4.12 Big Data Access and Analysis -- 4.13 Big Data Visualisation -- 4.14 Big Data Ingestion -- 4.15 Big Data Cluster Management -- 4.16 Big Data Distributions -- 4.17 Data Governance -- 4.18 Data Access -- 4.19 Data Analysis -- 4.20 Bid Data File System -- 4.20.1 Quantcast File System -- 4.20.2 Hadoop Distributed File System -- 4.20.3 Cassandra File System (CFS) -- 4.20.4 GlusterFS -- 4.20.5 Lustre -- 4.20.6 Parallel Virtual File System -- 4.20.7 Orange File System (OrangeFS) -- 4.20.8 BeeGFS -- 4.20.9 MapR-FS -- 4.20.9.1 Kudu -- References -- Chapter 5 Data-Driven Decision-Making -- 5.1 Data for Decision-Making -- 5.1.1 Data-Driven Decision-Making -- 5.1.2 The Process of Data-Driven Decision-Making -- 5.1.3 The Context of Data-Driven Decision-Making -- 5.1.4 The Importance of Data-Driven Decision-Making -- 5.1.5 Common Challenges of Data-Driven Decision-Making…”
    Libro electrónico
  11. 291
    Publicado 2015
    “…In this Analytic Data Storage in Hadoop training course, expert author Ryan Blue will teach you about typical storage and ingest patterns in Hadoop. This course is designed for users that are already familiar with Hadoop. …”
    Video
  12. 292
    Publicado 2021
    “…You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. …”
    Libro electrónico
  13. 293
    Publicado 1999
    Tabla de Contenidos: “…El centauro de la hipálage doble / Comicidad y parodia en la comedia burlesca del Siglo de Oro: El Hamete de Toledo, de tres ingenios / Amor y subjetividad en La Diana de Montemayor / Acerca de la Diana de Montemayor / Sor Juana, Pfandl, y la mujer masculina / Divulgación de un género literario en el Barroco / De los Hospitales de amor al Hospital de negios (de Boscán a Hurtado de Toledo) / El teatro barroco como juego La villana de Vallecas de Tirso de Molina / La yedra en la poesía de Francisco de la Torre: simbologia y autorepresentación / Sobre poética y retórica: la relación entre imitación, género y estilo en la obra de Francisco de Trillo y Figueroa / Abre el ojo y El amor al uso: marcas de género y configuración del «enredo» en Rojas Zorrilla y Antonio de Solís / Ingestión y expulsión - el problema de la identidad en el Lazarillo de Tormes / Vicente de la Rosa (Quijote I, 51) y el problema epistemológico de la verosimilitud / Agudeza emblemática en H. …”
    Libro electrónico
  14. 294
    Publicado 2021
    “…You'll work with use cases and practical examples to be able to ingest, process, analyze, and stream real-time data in no time…”
    Libro electrónico
  15. 295
    Publicado 2022
    “…In ancient times, people benefited from ingesting different parts of various weeds (root, stem, shoot, leaf, flower, fruit, seed, etc.) to maintain a healthy life. …”
    Libro electrónico
  16. 296
    Publicado 2018
    “…You’ll also learn how the ODH helps you leverage the investment in your data lake (or swamp), so that the data trapped there can finally be ingested, processed, and provisioned. With this ebook, you’ll learn how an ODH: Allows you to focus on categorizing data for easy and fast retrieval Provides flexible storage models, indexing support, query capabilities, security, and a governance framework Delivers flexible storage models; support for indexing, scripting, and automation; query capabilities; transactional integrity; and security Includes a governance model to help you access, ingest, harmonize, materialize, provision, and consume data…”
    Libro electrónico
  17. 297
    Publicado 2019
    “…Cumulus, a free and open source framework, supports this vision via configurable workflows to ingest, process, archive, manage, and distribute NASA's Earth imagery. …”
    Vídeo online
  18. 298
    Publicado 2023
    “…You'll learn how to scale your processing capabilities across multiple machines while ingesting data from any source--whether that's Hadoop clusters, cloud data storage, or local data files. …”
    Grabación no musical
  19. 299
    Publicado 2022
    “…TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. …”
    Libro electrónico
  20. 300
    por Wolsky, Tom
    Publicado 2008
    “…You develop a working knowledge with nineteen tutorials that cover each and every essential, including: * setting up your system and understanding the interface * ingesting and organizing your material including drive-based and disc-based camera data* slicing, dicing, and organizing clips * editing to build and trim a sequence of shots <BR i…”
    Libro electrónico