Tabla de Contenidos: “…Disciplina y problemas comunes de los cachorros -- Mordisqueo -- Mascar -- Saltos -- Posesividad -- Orines de sumisión -- Mareo en el coche -- Ingestión de deposiciones -- 18. Cuidado general del cachorro -- Alimentación adecuada de un cachorro -- ¿Qué es la comida para cachorros? …”

Tabla de Contenidos: “…Actuación ante casos de ingestión y/o intoxicación por el uso de productos de limpieza -- 12. …”

“…This book will introduce you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala. …”

Tabla de Contenidos: “…Handling configuration -- 13.5. Ingesting data -- 13.6. Testing -- 13.7. Real-time integration -- 13.8. …”

Tabla de Contenidos: “…-- Recommended Home Lab Setup -- Interactive Online Learning Environment and Test Bank -- DP-900 Exam Objectives -- Domain 1: Describe Core Data Components -- Subdomain 1a: Describe types of core data workloads -- Subdomain 1b: Describe data analytics core concepts -- Domain 2: Describe How to Work with Relational Data on Azure -- Subdomain 2a: Describe relational data workloads -- Subdomain 2b: Describe relational Azure data services -- Subdomain 2c: Identify basic management tasks for relational data -- Subdomain 2d: Describe query techniques for data using SQL language -- Domain 3: Describe How to Work with Nonrelational Data on Azure -- Subdomain 3a: Describe nonrelational data workloads -- Subdomain 3b: Describe nonrelational data offerings on Azure -- Subdomain 3c: Identify basic management tasks for nonrelational data -- Domain 4: Describe an Analytics Workload on Azure -- Subdomain 4a: Describe analytics workloads -- Subdomain 4b: Describe the components of a modern data warehouse -- Subdomain 4c: Describe data ingestion and processing on Azure -- Subdomain 4d: Describe data visualization in Microsoft Power BI -- Assessment Test -- Answers to the Assessment Test -- Chapter 1 Core Data Concepts -- Describe Types of Core Data Workloads -- Data Value -- Data Volume -- Data Variety -- Data Velocity -- Describe Data Analytics Core Concepts -- Data Processing Techniques -- Describe Analytics Techniques -- Describe Data Visualization Techniques -- Summary -- Exam Essentials -- Review Questions -- Chapter 2 Relational Databases in Azure -- Relational Database Features -- Relational Database Design Considerations…”

Tabla de Contenidos: “…Creating the parseDeviceInfo() function -- Forming the prepUSBLookup() function -- Constructing the getDeviceNames() function -- Enhancing the printOutput() function -- Running the script -- Additional challenges -- Summary -- Chapter 4: Working with Serialized Data Structures -- Serialized data structures -- A simple Bitcoin Web API -- Our first iteration - bitcoin_address_lookup.v1.py -- Exploring the main() function -- Understanding the getAddress() function -- Working with the printTransactions() function -- The printHeader() helper function -- The getInputs() helper function -- Running the script -- Our second iteration - bitcoin_address_lookup.v2.py -- Modifying the main() function -- Improving the getAddress() function -- Elaborating on the printTransactions() function -- Running the script -- Mastering our final iteration - bitcoin_address_lookup.py -- Enhancing the parseTransactions() function -- Developing the csvWriter() function -- Running the script -- Additional challenges -- Summary -- Chapter 5: Databases in Python -- An overview of databases -- Using SQLite3 -- Using the Structured Query Language -- Designing our script -- Manually manipulating databases with Python - file_lister.py -- Building the main() function -- Initializing the database with the initDB() function -- Checking for custodians with the getOrAddCustodian() function -- Retrieving custodians with the getCustodian() function -- Understanding the ingestDirectory() function -- Exploring the os.stat() method -- Developing the formatTimestamp() helper function -- Configuring the writeOutput() function -- Designing the writeCSV() function -- Composing the writeHTML() function -- Running the script -- Further automating databases - file_lister_peewee.py -- Peewee setup -- Jinja2 setup -- Updating the main() function -- Adjusting the initDB() function…”

Tabla de Contenidos: “…Bucketing on geo-spatial data -- Geo distance aggregation -- GeoHash grid aggregation -- Pipeline aggregations -- Calculating the cumulative sum of usage over time -- Summary -- Chapter 5: Analyzing Log Data -- Log analysis challenges -- Logstash -- Installation and configuration -- Prerequisites -- Downloading and installing Logstash -- Installing on Windows -- Installing on Linux -- Running Logstash -- Logstash architecture -- Overview of Logstash plugins -- Installing or updating plugins -- Input plugins -- Output plugins -- Filter plugins -- Codec plugins -- Exploring plugins -- Exploring Input plugins -- File -- Beats -- JDBC -- IMAP -- Output plugins -- Elasticsearch -- CSV -- Kafka -- PagerDuty -- Codec plugins -- JSON -- Rubydebug -- Multiline -- Filter plugins -- Ingest node -- Defining a pipeline -- Ingest APIs -- Put pipeline API -- Get Pipeline API -- Delete pipeline API -- Simulate pipeline API -- Summary -- Chapter 6: Building Data Pipelines with Logstash -- Parsing and enriching logs using Logstash -- Filter plugins -- CSV filter -- Mutate filter -- Grok filter -- Date filter -- Geoip filter -- Useragent filter -- Introducing Beats -- Beats by Elastic.co -- Filebeat -- Metricbeat -- Packetbeat -- Heartbeat -- Winlogbeat -- Auditbeat -- Community Beats -- Logstash versus Beats -- Filebeat -- Downloading and installing Filebeat -- Installing on Windows -- Installing on Linux -- Architecture -- Configuring Filebeat -- Filebeat prospectors -- Filebeat global options -- Filebeat general options -- Output configuration -- Filebeat modules -- Summary -- Chapter 7: Visualizing data with Kibana -- Downloading and installing Kibana -- Installing on Windows -- Installing on Linux -- Configuring Kibana -- Data preparation -- Kibana UI -- User interaction -- Configuring the index pattern -- Discover -- Elasticsearch query string…”

Tabla de Contenidos: “…Advanced driver assistance systems (ADAS) -- Summary -- Chapter 3: Exploring ML Algorithms -- Technical requirements -- How machines learn -- Overview of ML algorithms -- Consideration for choosing ML algorithms -- Algorithms for classification and regression problems -- Linear regression algorithms -- Logistic regression algorithms -- Decision tree algorithms -- Random forest algorithm -- Gradient boosting machine and XGBoost algorithms -- K-nearest neighbor algorithm -- Multi-layer perceptron (MLP) networks -- Algorithms for clustering -- Algorithms for time series analysis -- ARIMA algorithm -- DeepAR algorithm -- Algorithms for recommendation -- Collaborative filtering algorithm -- Multi-armed bandit/contextual bandit algorithm -- Algorithms for computer vision problems -- Convolutional neural networks -- ResNet -- Algorithms for natural language processing (NLP) problems -- Word2Vec -- BERT -- Generative AI algorithms -- Generative adversarial network -- Generative pre-trained transformer (GPT) -- Large Language Model -- Diffusion model -- Hands-on exercise -- Problem statement -- Dataset description -- Setting up a Jupyter Notebook environment -- Running the exercise -- Summary -- Chapter 4: Data Management for ML -- Technical requirements -- Data management considerations for ML -- Data management architecture for ML -- Data storage and management -- AWS Lake Formation -- Data ingestion -- Kinesis Firehose -- AWS Glue -- AWS Lambda -- Data cataloging -- AWS Glue Data Catalog -- Custom data catalog solution -- Data processing -- ML data versioning -- S3 partitions -- Versioned S3 buckets -- Purpose-built data version tools -- ML feature stores -- Data serving for client consumption -- Consumption via API -- Consumption via data copy -- Special databases for ML -- Vector databases -- Graph databases -- Data pipelines…”

Tabla de Contenidos: “…Redundancy and enabling business continuity -- Event retries and continuity -- Local data redundancy -- Infrastructure continuity -- Graceful communication between services -- Common results -- Summary -- Questions -- Further reading -- Chapter 13: Telemetry Capture and Integration -- Technical requirements -- Application- versus service- versus component-level telemetry -- Implementing non-intrusive telemetry capture -- Implementing custom telemetry capture -- Bubbling up meaningful information -- Metric ingestion with the Prometheus exporter -- Distributed tracing using the Jaeger exporter -- Summary -- Questions -- Further reading -- Chapter 14: Observability Revisited -- Technical requirements -- Sharing API services -- Generating service information -- OpenAPI specification -- A brief history of Swagger -- Generating the Swagger documentation -- Service discovery -- It's not quite load balancing... -- Exploring Consul -- Service discovery/registration -- Service resolution -- Service publishing -- Azure API Management -- Service cataloging -- Summary -- Questions -- Further reading -- Assessments -- Chapter 1, The Sample Application -- Chapter 2, The Producer-Consumer Pattern -- Chapter 3, Message Brokers -- Chapter 4, Domain Model and Asynchronous Events -- Chapter 5, Containerization and Local Environment Setup -- Chapter 6, Localized Testing and Debugging of Microservices -- Chapter 7, Microservice Observability -- Chapter 8, CI/CD Pipelines and Integrated Testing -- Chapter 9, Fault Injection and Chaos Testing -- Chapter 10, Modern Design Patterns for Scalability -- Chapter 11, Minimizing Data Loss -- Chapter 12, Service and Application Resiliency -- Chapter 13, Telemetry Capture and Integration -- Chapter 14, Observability Revisited -- Index -- Other Books You May Enjoy…”

Tabla de Contenidos: “…-- Connecting to your data warehouse -- Using Amazon Redshift query editor v2 -- Loading sample data -- Running your first query -- Summary -- Chapter 2: Data Loading and Analytics on Redshift Serverless -- Technical requirements -- Data loading using Amazon Redshift Query Editor v2 -- Creating tables -- Loading data from Amazon S3 -- Loading data from a local drive -- Data loading from Amazon S3 using the COPY command -- Loading data from a Parquet file -- Automating file ingestion with a COPY job -- Best practices for the COPY command -- Data loading using the Redshift Data API -- Creating table -- Loading data using the Redshift Data API -- Summary -- Chapter 3: Applying Machine Learning in Your Data Warehouse -- Understanding the basics of ML -- Comparing supervised and unsupervised learning -- Classification -- Regression -- Traditional steps to implement ML -- Data preparation -- Evaluating an ML model -- Overcoming the challenges of implementing ML today -- Exploring the benefits of ML -- Summary -- Part 2: Getting Started with Redshift ML -- Chapter 4: Leveraging Amazon Redshift ML -- Why Amazon Redshift ML? …”

Tabla de Contenidos: “…DEFINICIÓN, ORÍGENES Y ACCIONES -- B ENVENENAMIENTOS POR INGESTIÓN -- C RECOMENDACIONES -- D EMÉTICOS. EMOLIENTES. -- ANTÍDOTOS -- NIVERSAL.ANTÍDOTOS -- E ENVENENAMIENTOS POR VÍA CUTÁNEO- MUCOSA -- F ENVENENAMIENTOS POR INHALACIÓN -- 18 INYECCIONES -- A TÉCNICAS ELEMENTALES -- B LUGAR Y FORMA DE INYECTAR(IM) -- 19. …”

Tabla de Contenidos: “…-- Elasticsearch installation -- Installation of Elasticsearch -- Create index -- Primary shard -- Replica shard -- Ingest documents into index -- Bulk Insert -- Document search -- Meta fields -- Mapping -- Static mapping -- Dynamic mapping -- Elasticsearch-supported data types -- Mapping example -- Analyzer -- Elasticsearch stack components…”

Tabla de Contenidos: “…SUMMARY AND RECOMMENDATIONS -- REFERENCES -- 4 Measurement of Exposure to Asbestiform Fibers -- MEASUREMENT TECHNIQUES -- MEASURING ASBESTOS DUST IN THE WORKPLACE -- The Impinger Technique -- The Membrane Filter Technique -- MEASURING ASBESTOS DUST IN THE AMBIENT ENVIRONMENT -- RELATIONSHIPS AMONG VARIOUS EXPOSURE MEASUREMENT METHODS -- EXPOSURE TO CHRYSOTILE IN THE AMBIENT ENVIRONMENT -- COMPLICATING FACTORS IN ENVIRONMENTAL ASSAYS -- FUTURE MEASUREMENT OF EXPOSURE TO ASBESTIFORM FIBERS -- RECOMMENDATIONS -- REFERENCES -- 5 Effects of Asbestiform Fibers on Human Health -- NATURE OF EVIDENCE -- BIODISPOSITION OF FIBERS -- Fiber Deposition -- Clearance and Transport -- CLINICAL ASPECTS OF ASBESTOS-ASSOCIATED DISEASES -- Necessary Assumptions Used in Determining Health Effects -- Sensitivity and Specificity of Clinical Evidence -- General Diagnostic Measures -- DISEASE ASSOCIATED WITH NONOCCUPATIONAL INHALATION EXPOSURES TO ASBESTIFORM FIBERS -- Asbestos Exposure from Household Contacts -- Neighborhood Exposure to Asbestos -- Natural Sources of Asbestiform Fibers -- Summary -- EPIDEMIOLOGICAL STUDIES OF EFFECTS RESULTING FROM THE INGESTION OF ASBESTOS IN DRINKING WATER -- OCCUPATIONAL EPIDEMIOLOGICAL STUDIES-METHODOLOGICAL CONSIDERATIONS -- CANCER MORTALITY IN OCCUPATIONAL COHORTS EXPOSED TO ASBESTOS -- Mining and Milling -- Manufacturing -- Insulation -- Shipyards -- Relative Carcinogenicity of Different Types of Asbestos -- Effects of Smoking -- Summary -- ASBESTOSIS AND ASBESTOS-ASSOCIATED PLEURAL DISEASE IN OCCUPATIONAL COHORTS -- Mortality Studies -- Morbidity Studies -- Summary -- HEALTH EFFECTS OF OCCUPATIONAL EXPOSURE TO MAN-MADE MINERAL FIBERS12 -- Morbidity -- Mortality -- Summary -- ADDITIONAL OCCUPATIONAL EPIDEMIOLOGICAL STUDIES -- Attapulgite -- Talc -- RECOMMENDATIONS -- REFERENCES…”

Tabla de Contenidos: “…Biologic Markers in Immunotoxicology -- Copyright -- Preface -- Contents -- List of Abbreviations -- Summary -- HYPERSENSITIVITY -- AUTOIMMUNITY -- IMMUNE SUPPRESSION -- BIOASSAYS OF IMMUNOTOXICITY -- CLINICAL APPLICATION OF EXISTING IMMUNOTOXICOLOGIC BIOLOGIC MARKERS -- ROLE OF BIOLOGIC MARKERS OF IMMUNOTOXICITY IN EPIDEMIOLOGY -- INDOOR AIR POLLUTION AND MULTIPLE CHEMICAL SENSITIVITY -- 1 Introduction -- BIOLOGIC MARKERS -- Markers of Exposure -- Markers of Effect -- Markers of Susceptibility -- VALIDITY OF BIOLOGIC MARKERS -- UNCERTAINTY AND RISK -- ETHICAL AND PRACTICAL ISSUES -- STRUCTURE OF THE REPORT -- 2 The Structure and Function Of the Immune System And Mechanisms of Immunotoxicity -- DEVELOPMENT AND FUNCTION OF THE IMMUNE SYSTEM -- MECHANISMS OF CHEMICALLY INDUCED IMMUNE DISEASE -- IgE-Mediated Hypersensitivity -- Complement-Mediated and Immune-Complex-Mediated Injury -- T-Cell Reactions -- EFFECTS OF XENOBIOTICS ON THE IMMUNE SYSTEM -- 3 Biologic Markers For Immune-Mediated Disease -- DEFINITION OF THE PROBLEM -- EXPOSURE THROUGH INHALATION (PULMONARY HYPERSENSITIVITY) -- Occupational Asthma and Rhinitis -- Hypersensitivity Pneumonitis -- EXPOSURE THROUGH INGESTION -- DERMAL EXPOSURE -- Contact Dermatitis -- Photocontact Dermatitis -- Contact Urticaria -- NONSPECIFIC IMMUNE ENHANCEMENT -- BIOLOGIC MARKERS OF HYPERSENSITIVITY -- History and Clinical Signs -- Skin Tests -- Serum IgE Concentration -- In Vitro Assays for Specific Antibody -- In Vitro Assays for Cellular Immunity -- Provocation Challenges -- Bronchoalveolar Lavage -- ANIMAL MODELS FOR DETECTING CHEMICALLY MEDIATED HYPERSENSITIVITY -- Techniques Used in Animal Studies to Determine the Potential of Chemicals to Cause Hypersensitivity -- IgE-and IgG-Mediated Immediate Reactions -- Cytotoxic Reactions -- Antigen-Antibody Complexes -- Contact Hypersensitivity -- SUMMARY…”

Tabla de Contenidos: “…Getting hands-on with AWS Glue ETL components -- Querying S3 data using Athena -- Processing real-time data using Kinesis data streams -- Storing and transforming real-time data using Kinesis Data Firehose -- Different ways of ingesting data from on-premises into AWS -- AWS Storage Gateway -- Snowball, Snowball Edge, and Snowmobile -- AWS DataSync -- Processing stored data on AWS -- AWS EMR -- AWS Batch -- Summary -- Questions -- Answers -- Section 3: Data Modeling -- Chapter 7: Applying Machine Learning Algorithms -- Introducing this chapter -- Storing the training data -- A word about ensemble models -- Supervised learning -- Working with regression models -- Working with classification models -- Forecasting models -- Object2Vec -- Unsupervised learning -- Clustering -- Anomaly detection -- Dimensionality reduction -- IP Insights -- Textual analysis -- Blazing Text algorithm -- Sequence-to-sequence algorithm -- Neural Topic Model (NTM) algorithm -- Image processing -- Image classification algorithm -- Semantic segmentation algorithm -- Object detection algorithm -- Summary -- Questions -- Chapter 8: Evaluating and Optimizing Models -- Introducing model evaluation -- Evaluating classification models -- Extracting metrics from a confusion matrix -- Summarizing precision and recall -- Evaluating regression models -- Exploring other regression metrics -- Model optimization -- Grid search -- Summary -- Questions -- Chapter 9: Amazon SageMaker Modeling -- Technical requirements -- Creating notebooks in Amazon SageMaker -- What is Amazon SageMaker? …”

Tabla de Contenidos: “…-- What RAW Means and How to Digitally Process It -- Compressed File Systems -- Uncompressed File Systems -- Common Camera Media Types -- Common File Formats for Cameras -- Common Production Formats -- What Ingest Means and How to Organize It -- Asset Management -- On-Set Grading -- Dailies -- Sources and Types of Metadata -- The Role of the DIT -- The Role of the Data Wrangler -- eXtensible Markup Language ( XML) -- Key XML Terminology -- Cloud Impacts -- 4 Post-Production -- Introduction -- Post-Production in Detail -- Preparation -- Technical Preparation -- Post-Production Specific for TV -- The End Game! …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Planning Power BI Projects -- Power BI deployment modes -- Corporate BI -- Self-Service Visualization -- Self-Service BI -- Choosing a deployment mode -- Project discovery and ingestion -- Sample Power BI project template -- Sample template - Adventure Works BI -- Power BI project roles -- Dataset designer -- Report authors -- Power BI admin -- Project role collaboration -- Power BI licenses -- Power BI license scenarios -- Power BI Premium features -- Data warehouse bus matrix -- Dataset design process -- Selecting the business process -- Declaring the grain -- Identifying the dimensions -- Defining the facts -- Data profiling -- Dataset planning -- Data transformations -- Import versus DirectQuery -- Import mode -- DirectQuery mode -- Sample project analysis -- Summary -- Chapter 2: Connecting to Sources and Transforming Data with M -- Query design per dataset mode -- Import mode dataset queries -- DirectQuery dataset queries -- Data sources -- Authentication -- Data source settings -- Privacy levels -- Power BI as a data source -- Power BI Desktop options -- Global options -- CURRENT FILE options -- SQL views -- SQL views versus M queries -- SQL view examples -- Date dimension view -- Mark As Date Table -- Product Dimension view -- Slowly-changing dimensions -- M queries -- Data Source Parameters -- Staging Queries -- DirectQuery staging -- Fact and dimension queries -- Source Reference Only -- M query summary -- Excel workbook - Annual Sales Plan -- Data types -- Item access in M -- DirectQuery report execution -- Bridge Tables Queries -- Parameter Tables -- Security Tables -- Query folding -- Partial query folding -- M Query examples -- Trailing three years filter -- Customer history column…”

“…With Apache Spark as the foundation, you will follow a step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data platform running Apache Spark, Apache Zeppelin, Apache Kafka, Redis, MySQL, Minio (S3), and Apache Airflow…”

“…Ils contiennent des matières radioactives, dont l’exposition externe aux rayonnements, l’ingestion ou l’inhalation accidentelle pourraient constituer des dangers. …”

Tabla de Contenidos: “…-- The pain point of IoT before the cloud -- The impact of the cloud -- Integrating IoT with the cloud -- Device management -- Data ingestion -- Communication protocols between IoT devices and the cloud -- MQTT -- LwM2M -- CoAP -- AWS for IoT -- AWS IoT Core -- AWS IoT Device Management -- AWS IoT Device Defender -- AWS IoT Analytics -- AWS Lambda -- AWS Kinesis -- AWS DynamoDB -- AWS QuickSight -- Summary -- Part 2: Utilizing AI in IoT Development -- Chapter 6: Applying ChatGPT in the IoT Innovation Journey -- Important tips -- Reshaping the future with AI -- Utilizing ChatGPT in IoT development process -- Interacting with ChatGPT properly -- Best practices for beginner IoT projects -- Prompt framework options -- Best practice prompt examples -- Generating code snippets on ESP32 -- Summary -- Chapter 7: Recommendations to Start Your First IoT Project -- Technical requirements -- Thinking big and starting small -- Reaching out for the low-hanging fruits first -- Summary…”