Genesis es una empresa fundada en 2011 que se especializa en la importación y venta de calzados femeninos al por mayor y menor. La empresa tiene 6 empleados y está ubicada en la ciudad de Santa Cruz, Bolivia. Se enfoca en satisfacer las necesidades de los clientes y generar empleos. Su visión es convertirse en una empresa prestigiosa que impone innovación en calzados femeninos mediante la expansión a diferentes sucursales.
This document provides an overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It discusses NLTK's modules for common NLP tasks like tokenization, part-of-speech tagging, parsing, and classification. It also describes how NLTK can be used to analyze text corpora, frequency distributions, collocations and concordances. Key functions of NLTK include tokenizing text, accessing annotated corpora, analyzing word frequencies, part-of-speech tagging, and shallow parsing.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It implements Google's MapReduce programming model and the Hadoop Distributed File System (HDFS) for reliable data storage. Key components include a JobTracker that coordinates jobs, TaskTrackers that run tasks on worker nodes, and a NameNode that manages the HDFS namespace and DataNodes that store application data. The framework provides fault tolerance, parallelization, and scalability.
This document provides an overview of the Python programming language. It discusses Python's history and evolution, its key features like being object-oriented, open source, portable, having dynamic typing and built-in types/tools. It also covers Python's use for numeric processing with libraries like NumPy and SciPy. The document explains how to use Python interactively from the command line and as scripts. It describes Python's basic data types like integers, floats, strings, lists, tuples and dictionaries as well as common operations on these types.
This document provides an overview of the statistical programming language R. It discusses key R concepts like data types, vectors, matrices, data frames, lists, and functions. It also covers important R tools for data analysis like statistical functions, linear regression, multiple regression, and file input/output. The goal of R is to provide a large integrated collection of tools for data analysis and statistical computing.
Spark is an open-source cluster computing framework that uses in-memory processing to allow data sharing across jobs for faster iterative queries and interactive analytics, it uses Resilient Distributed Datasets (RDDs) that can survive failures through lineage tracking and supports programming in Scala, Java, and Python for batch, streaming, and machine learning workloads.
This document provides an overview of NoSQL databases and summarizes key information about several NoSQL databases, including HBase, Redis, Cassandra, MongoDB, and Memcached. It discusses concepts like horizontal scalability, the CAP theorem, eventual consistency, and data models used by different NoSQL databases like key-value, document, columnar, and graph structures.
Genesis es una empresa fundada en 2011 que se especializa en la importación y venta de calzados femeninos al por mayor y menor. La empresa tiene 6 empleados y está ubicada en la ciudad de Santa Cruz, Bolivia. Se enfoca en satisfacer las necesidades de los clientes y generar empleos. Su visión es convertirse en una empresa prestigiosa que impone innovación en calzados femeninos mediante la expansión a diferentes sucursales.
This document provides an overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It discusses NLTK's modules for common NLP tasks like tokenization, part-of-speech tagging, parsing, and classification. It also describes how NLTK can be used to analyze text corpora, frequency distributions, collocations and concordances. Key functions of NLTK include tokenizing text, accessing annotated corpora, analyzing word frequencies, part-of-speech tagging, and shallow parsing.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It implements Google's MapReduce programming model and the Hadoop Distributed File System (HDFS) for reliable data storage. Key components include a JobTracker that coordinates jobs, TaskTrackers that run tasks on worker nodes, and a NameNode that manages the HDFS namespace and DataNodes that store application data. The framework provides fault tolerance, parallelization, and scalability.
This document provides an overview of the Python programming language. It discusses Python's history and evolution, its key features like being object-oriented, open source, portable, having dynamic typing and built-in types/tools. It also covers Python's use for numeric processing with libraries like NumPy and SciPy. The document explains how to use Python interactively from the command line and as scripts. It describes Python's basic data types like integers, floats, strings, lists, tuples and dictionaries as well as common operations on these types.
This document provides an overview of the statistical programming language R. It discusses key R concepts like data types, vectors, matrices, data frames, lists, and functions. It also covers important R tools for data analysis like statistical functions, linear regression, multiple regression, and file input/output. The goal of R is to provide a large integrated collection of tools for data analysis and statistical computing.
Spark is an open-source cluster computing framework that uses in-memory processing to allow data sharing across jobs for faster iterative queries and interactive analytics, it uses Resilient Distributed Datasets (RDDs) that can survive failures through lineage tracking and supports programming in Scala, Java, and Python for batch, streaming, and machine learning workloads.
This document provides an overview of NoSQL databases and summarizes key information about several NoSQL databases, including HBase, Redis, Cassandra, MongoDB, and Memcached. It discusses concepts like horizontal scalability, the CAP theorem, eventual consistency, and data models used by different NoSQL databases like key-value, document, columnar, and graph structures.
Graph databases store data in graph structures with nodes, edges, and properties. Neo4j is a popular open-source graph database that uses a property graph model. It has a core API for programmatic access, indexes for fast lookups, and Cypher for graph querying. Neo4j provides high availability through master-slave replication and scales horizontally by sharding graphs across instances through techniques like cache sharding and domain-specific sharding.
This document discusses information retrieval techniques. It begins by defining information retrieval as selecting the most relevant documents from a large collection based on a query. It then discusses some key aspects of information retrieval including document representation, indexing, query representation, and ranking models. The document also covers specific techniques used in information retrieval systems like parsing documents, tokenization, removing stop words, normalization, stemming, and lemmatization.
This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
This document provides an overview of recommender systems for e-commerce. It discusses various recommender approaches including collaborative filtering algorithms like nearest neighbor methods, item-based collaborative filtering, and matrix factorization. It also covers content-based recommendation, classification techniques, addressing challenges like data sparsity and scalability, and hybrid recommendation approaches.
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
En la ciudad de Pasto, estamos revolucionando el acceso a microcréditos y la formalización de microempresarios informales con nuestra aplicación CrediAvanza. Nuestro objetivo es empoderar a los emprendedores locales proporcionándoles una plataforma integral que facilite el acceso a servicios financieros y asesoría profesional.
Ofrecemos herramientas y metodologías para que las personas con ideas de negocio desarrollen un prototipo que pueda ser probado en un entorno real.
Cada miembro puede crear su perfil de acuerdo a sus intereses, habilidades y así montar sus proyectos de ideas de negocio, para recibir mentorías .
Graph databases store data in graph structures with nodes, edges, and properties. Neo4j is a popular open-source graph database that uses a property graph model. It has a core API for programmatic access, indexes for fast lookups, and Cypher for graph querying. Neo4j provides high availability through master-slave replication and scales horizontally by sharding graphs across instances through techniques like cache sharding and domain-specific sharding.
This document discusses information retrieval techniques. It begins by defining information retrieval as selecting the most relevant documents from a large collection based on a query. It then discusses some key aspects of information retrieval including document representation, indexing, query representation, and ranking models. The document also covers specific techniques used in information retrieval systems like parsing documents, tokenization, removing stop words, normalization, stemming, and lemmatization.
This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
This document provides an overview of recommender systems for e-commerce. It discusses various recommender approaches including collaborative filtering algorithms like nearest neighbor methods, item-based collaborative filtering, and matrix factorization. It also covers content-based recommendation, classification techniques, addressing challenges like data sparsity and scalability, and hybrid recommendation approaches.
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
En la ciudad de Pasto, estamos revolucionando el acceso a microcréditos y la formalización de microempresarios informales con nuestra aplicación CrediAvanza. Nuestro objetivo es empoderar a los emprendedores locales proporcionándoles una plataforma integral que facilite el acceso a servicios financieros y asesoría profesional.
Ofrecemos herramientas y metodologías para que las personas con ideas de negocio desarrollen un prototipo que pueda ser probado en un entorno real.
Cada miembro puede crear su perfil de acuerdo a sus intereses, habilidades y así montar sus proyectos de ideas de negocio, para recibir mentorías .
Soluciones Examen de Selectividad. Geografía junio 2024 (Convocatoria Ordinar...Juan Martín Martín
Criterios de corrección y soluciones al examen de Geografía de Selectividad (EvAU) Junio de 2024 en Castilla La Mancha.
Soluciones al examen.
Convocatoria Ordinaria.
Examen resuelto de Geografía
conocer el examen de geografía de julio 2024 en:
https://blogdegeografiadejuan.blogspot.com/2024/06/soluciones-examen-de-selectividad.html
http://blogdegeografiadejuan.blogspot.com/