Machine Learning Con Pytorch Y Scikit Learn

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Machine Learning Con Pytorch Y Scikit Lean

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Author : Sebastian Raschka
language : es
Publisher:
Release Date : 2023

Machine Learning Con Pytorch Y Scikit Lean written by Sebastian Raschka and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

Si busca un manual de referencia sobre Machine Learning y Deep Learning con PyTorch, ha llegado al libro indicado. En él se explica paso a paso cómo construir sistemas de aprendizaje automático con éxito.Mientras que en algunos libros solo se enseña a seguir instrucciones, en este descubrirá los principios para crear modelos y aplicaciones por sí mismo. Encontrará multitud de explicaciones claras, visualizaciones y ejemplos, y aprenderá en profundidad todas las técnicas esenciales de Machine Learning.Actualizado para ocuparse de Machine Learning utilizando PyTorch, este libro también presenta las últimas incorporaciones a Scikit-Learn. Además, trata varias técnicas de Machine Learning y Deep Learning para la clasificación de textos e imágenes. Con este libro, también aprenderá sobre las redes generativas antagónicas (GAN), útiles para generar nuevos datos y entrenar agentes inteligentes con aprendizaje reforzado.Por último, esta edición incluye las últimas tendencias en Machine Learning, como las introducciones a las redes neuronales de grafos y transformadores a gran escala utilizados para el procesamiento del lenguaje natural (NLP).Sin duda, tanto si es un desarrollador de Python neófito en Machine Learning como si desea profundizar en los últimos avances, este libro de PyTorch será su gran aliado en el aprendizaje automático con Python.

Machine Learning With Pytorch And Scikit Learn

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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-02-25

Machine Learning With Pytorch And Scikit Learn written by Sebastian Raschka and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-25 with Computers categories.

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Machine Learning Con Pytorch Y Scikit Learn

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Author : Sebastian Raschka
language : es
Publisher: Marcombo
Release Date : 2023-02-27

Machine Learning Con Pytorch Y Scikit Learn written by Sebastian Raschka and has been published by Marcombo this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-27 with Computers categories.

Si busca un manual de referencia sobre Machine Learning y Deep Learning con PyTorch, ha llegado al libro indicado. En él se explica paso a paso cómo construir sistemas de aprendizaje automático con éxito. Mientras que en algunos libros solo se enseña a seguir instrucciones, en este descubrirá los principios para crear modelos y aplicaciones por sí mismo. Encontrará multitud de explicaciones claras, visualizaciones y ejemplos, y aprenderá en profundidad todas las técnicas esenciales de Machine Learning. Actualizado para ocuparse de Machine Learning utilizando PyTorch, este libro también presenta las últimas incorporaciones a Scikit-Learn. Además, trata varias técnicas de Machine Learning y Deep Learning para la clasificación de textos e imágenes. Con este libro, también aprenderá sobre las redes generativas antagónicas (GAN), útiles para generar nuevos datos y entrenar agentes inteligentes con aprendizaje reforzado. Por último, esta edición incluye las últimas tendencias en Machine Learning, como las introducciones a las redes neuronales de grafos y transformadores a gran escala utilizados para el procesamiento del lenguaje natural (NLP). Sin duda, tanto si es un desarrollador de Python neófito en Machine Learning como si desea profundizar en los últimos avances, este libro de PyTorch será su gran aliado en el aprendizaje automático con Python. «Estoy seguro de que este libro le resultará muy valioso, tanto por ofrecer una visión general del apasionante campo de Machine Learning, como por ser un tesoro de conocimientos prácticos. Espero que le inspire a aplicar Machine Learning para lograr un mayor beneficio, sea cual sea su problemática» Gracias a esta lectura: •Explorará marcos de trabajo, modelos y técnicas para que las máquinas «aprendan» de los datos •Empleará Scikit-Learn para Machine Learning y PyTorch para Deep Learning •Entrenará clasificadores de Machine Learning en imágenes, texto, etc. •Creará y entrenará redes neuronales, transformadores y redes neuronales gráficas •Descubrirá las mejores prácticas para evaluar y ajustar los modelos •Pronosticará los resultados de elementos continuos utilizando el análisis de regresión •Profundizará en los datos textuales y de las redes sociales mediante el análisis de sentimiento

El Machine Learning Y La Inteligencia Artificial

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Author : Sebastian Raschka
language : es
Publisher: Marcombo
Release Date : 2024-10-23

El Machine Learning Y La Inteligencia Artificial written by Sebastian Raschka and has been published by Marcombo this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-23 with Computers categories.

Si está listo para aventurarse más allá de los conceptos introductorios e indagar en el aprendizaje automático, en el aprendizaje profundo y en la inteligencia artificial (IA), el formato de preguntas y respuestas que presenta el libro El Machine Learning y la IA le facilitará mucho las cosas. Nacido de las cuestiones que a menudo se plantea el autor, Sebastián Raschka, este libro muestra un método directo y sin rodeos para acercarle a temas avanzados, que presenta de forma rápida y accesible. Cada capítulo es breve y autónomo, y aborda una cuestión fundamental de la IA, desvelándola con explicaciones claras, diagramas y ejercicios prácticos. En esta lectura encontrará: CAPÍTULOS CONCISOS: Las preguntas clave de la IA se responden de forma sencilla y las ideas complejas se desglosan en piezas fáciles de digerir. GAMA AMPLIA DE TEMAS: Raschka cubre temas que van desde la arquitectura de las redes neuronales y la evaluación de los modelos hasta la visión informática y el procesamiento del lenguaje natural. USOS PRÁCTICOS: Conocerá técnicas para mejorar el rendimiento de los modelos, afinar modelos grandes y mucho más. También aprenderá a: "Gestionar las distintas fuentes de aleatoriedad en la formación de redes neuronales "Diferenciar entre arquitecturas de codificador y decodificador en modelos de lenguaje grandes "Reducir el sobreajuste con modificaciones de datos y modelos "Construir intervalos de confianza para clasificadores y optimizar los modelos con datos etiquetados limitados "Elegir entre paradigmas distintos de formación multi-GPU y tipos diferentes de modelos de IA generativa "Comprender las métricas de rendimiento para el procesamiento del lenguaje natural "Dar sentido a los sesgos inductivos en los transformadores de visión Si busca el recurso perfecto para mejorar su comprensión del aprendizaje automático, El Machine Learning y la IA le ayudará a avanzar fácilmente en este camino.

Inteligencia Artificial Fundamentos Aplicaciones Y Futuro

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Author : María Dolores, Pérez Ramírez
language : en
Publisher: ICB Editores
Release Date : 2024-09-18

Inteligencia Artificial Fundamentos Aplicaciones Y Futuro written by María Dolores, Pérez Ramírez and has been published by ICB Editores this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-18 with Computers categories.

Esta obra ofrece una inmersión profunda en el mundo de la inteligencia artificial (IA), abarcando desde los principios básicos hasta sus aplicaciones más disruptivas en sectores clave como la robótica, la salud, el entretenimiento y la movilidad. A lo largo de la obra, se exploran técnicas avanzadas como el aprendizaje automático y el procesamiento de lenguaje natural (PLN), proporcionando una base sólida para comprender el estado actual de esta tecnología y sus posibilidades futuras. El texto no solo examina las metodologías y plataformas utilizadas para desarrollar soluciones de IA, sino también el impacto económico y social que esta tecnología está generando. Desde la interacción humano-computadora hasta los desafíos éticos y de seguridad que plantea, el libro invita a reflexionar sobre el papel de la IA en la transformación global de industrias y en las relaciones humanas. Inteligencia Artificial: Fundamentos, Aplicaciones y Futuro es una guía imprescindible para aquellos interesados en entender cómo la IA está remodelando el futuro, mientras considera las implicaciones éticas y de transparencia necesarias en su desarrollo y aplicación.

Computer Science Cacic 2022

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Author : Patricia Pesado
language : en
Publisher: Springer Nature
Release Date : 2023-05-26

Computer Science Cacic 2022 written by Patricia Pesado and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-26 with Computers categories.

This book constitutes the refereed proceedings of the 28th Argentine Congress on Computer Science, CACIC 2022, held in La Rioja, Argentina, during October 3–6, 2022. The 20 full papers included in this book were carefully reviewed and selected from 184 submissions. They were organized in topical sections as follows: Agents and Systems; Technology Applied to Education; Graphic Computation, Images and Visualization; Software Engineering; Databases and Data Mining; Hardware Architectures, Networks, and Operating Systems; Innovation in Software Systems; Signal Processing and Real-Time Systems; Innovation in Computer Science Education; and Digital Goverance and Smart Cities.

Machine Learning Crash Course For Engineers

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Author : Eklas Hossain
language : en
Publisher: Springer Nature
Release Date : 2023-12-26

Machine Learning Crash Course For Engineers written by Eklas Hossain and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-26 with Mathematics categories.

​Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly.

Machine Learning And Artificial Intelligence In Chemical And Biological Sensing

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Author : Jeong-Yeol Yoon
language : en
Publisher: Elsevier
Release Date : 2024-07-07

Machine Learning And Artificial Intelligence In Chemical And Biological Sensing written by Jeong-Yeol Yoon and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-07 with Science categories.

Machine learning (ML) has recently become popular in chemical and biological sensing applications. ML is a subset of artificial intelligence (AI) and other AI techniques have been used in various chemical and biological sensing. Machine Learning and Artificial Intelligence in Chemical and Biological Sensing covers the theoretical background and practical applications of various ML/AI methods toward chemical and biological sensing. No comprehensive reference text has been available previously to cover the wide breadth of this topic. The Editors have written the first three chapters to firmly introduce the reader to fundamental ML theories that can be used for chemical/biosensing. The subsequent chapters then cover the practical applications with contributions by various experts in the field. They show how ML and AI-based techniques can provide solutions for: 1) identifying and quantifying target molecules when specific receptors are unavailable 2) analyzing complex mixtures of target molecules, such as gut microbiome and soil microbiome3) analyzing high-throughput and high-dimensional data, such as drug screening, molecular interaction, and environmental toxicant analysis, 4) analyzing complex data sets where fingerprinting approach is needed This book is written primarily for upper undergraduate students, graduate students, research staff, and faculty members at teaching and research universities and colleges who are working on chemical sensing, biosensing, analytical chemistry, analytical biochemistry, biomedical imaging, medical diagnostics, environmental monitoring, and agricultural applications. - Presents the first comprehensive reference text on the use of ML and AI for chemical and biological sensing - Provides a firm grounding in the fundamental theories on ML and AI before covering the practical applications with contributions by various experts in the field - Includes a wide array of practical applications covered, including: E-nose, Raman, SERS, lens-free imaging, multi/hyperspectral imaging, NIR/optical imaging, receptor-free biosensing, paper microfluidics, single molecule analysis in biomedicine, in situ protein characterization, microbial population dynamics, and all-in-one sensor systems

Computer Vision Eccv 2024

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Author : Aleš Leonardis
language : en
Publisher: Springer Nature
Release Date : 2024-10-25

Computer Vision Eccv 2024 written by Aleš Leonardis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-25 with Computers categories.

The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024. The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Deep Learning For Engineers

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Author : Tariq M. Arif
language : en
Publisher: CRC Press
Release Date : 2024-02-28

Deep Learning For Engineers written by Tariq M. Arif and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-28 with Computers categories.

Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the basic elements required for understanding and applying deep learning models. As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of convolutional neural network (CNN) and recurrent neural network (RNN) architectures and their practical implementations in science and engineering are also discussed. This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful.