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Titre : Data Engineering with Python Type de document : e-book Auteurs : Paul CRICKARD Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781839214189 Note générale : copyrighted Langues : Anglais (eng) Résumé : Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projectsKey FeaturesBecome well-versed in data architectures, data preparation, and data optimization skills with the help of practical examplesDesign data models and learn how to extract, transform, and load (ETL) data using PythonSchedule, automate, and monitor complex data pipelines in productionBook DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python.The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You'll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You'll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you'll build architectures on which you'll learn how to deploy data pipelines.By the end of this Python book, you'll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learnUnderstand how data engineering supports data science workflowsDiscover how to extract data from files and databases and then clean, transform, and enrich itConfigure processors for handling different file formats as well as both relational and NoSQL databasesFind out how to implement a data pipeline and dashboard to visualize resultsUse staging and validation to check data before landing in the warehouseBuild real-time pipelines with staging areas that perform validation and handle failuresGet to grips with deploying pipelines in the production environmentWho this book is forThis book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88906445 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=526037 DAX Cookbook : Over 120 recipes to enhance your business with analytics, reporting, and business intelligence / Greg DECKLER / PACKT PUBLISHING (2020)
Titre : DAX Cookbook : Over 120 recipes to enhance your business with analytics, reporting, and business intelligence Type de document : e-book Auteurs : Greg DECKLER Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781839217074 Note générale : copyrighted Langues : Anglais (eng) Résumé : Solve real-world business problems by learning how to create common industry key performance indicators and other calculations using DAX within Microsoft products such as Power BI, SQL Server, and Excel. Key Features Learn to write sophisticated DAX queries to solve business intelligence and data analytics challenges Handle performance issues and optimization within the data model, DAX calculations and more Solve business issues with Microsoft Excel, Power BI, and SQL Server using DAX queries Book Description DAX provides an extra edge by extracting key information from the data that is already present in your model. Filled with examples of practical, real-world calculations geared toward business metrics and key performance indicators, this cookbook features solutions that you can apply for your own business analysis needs. You'll learn to write various DAX expressions and functions to understand how DAX queries work. The book also covers sections on dates, time, and duration to help you deal with working days, time zones, and shifts. You'll then discover how to manipulate text and numbers to create dynamic titles and ranks, and deal with measure totals. Later, you'll explore common business metrics for finance, customers, employees, and projects. The book will also show you how to implement common industry metrics such as days of supply, mean time between failure, order cycle time and overall equipment effectiveness. In the concluding chapters, you'll learn to apply statistical formulas for covariance, kurtosis, and skewness. Finally, you'll explore advanced DAX patterns for interpolation, inverse aggregators, inverse slicers, and even forecasting with a deseasonalized correlation coefficient. By the end of this book, you'll have the skills you need to use DAX's functionality and flexibility in business intelligence and data analytics. What you will learn Understand how to create common calculations for dates, time, and duration Create key performance indicators (KPIs) and other business calculations Develop general DAX calculations that deal with text and numbers Discover new ideas and time-saving techniques for better calculations and models Perform advanced DAX calculations for solving statistical measures and other mathematical formulas Handle errors in DAX and learn how to debug DAX calculations Understand how to optimize your data models Who this book is for Business users, BI developers, data analysts, and SQL users who are looking for solutions to the challenges faced while solving analytical operations using DAX techniques and patterns will find this book useful. Basic knowledge of the DAX language and Microsoft services is mandatory. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88884055 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=581609
Titre : Deep Learning for Beginners Type de document : e-book Auteurs : Dr. Pablo RIVAS Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781838640859 Note générale : copyrighted Langues : Anglais (eng) Résumé : Implementing supervised, unsupervised, and generative deep learning (DL) models using Keras and Dopamine over TensorFlow.Key FeaturesUnderstand the fundamental machine learning concepts useful in deep learningLearn the underlying mathematical concepts as you implement deep learning models from scratchExplore easy-to-understand examples and use cases that will help you build a solid foundation in DLBook DescriptionWith information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and already have the basic mathematical and programming knowledge required to get started.The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples, and you will even build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book.By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks.What you will learnImplement RNNs and Long short-term memory for image classification and Natural Language Processing tasksExplore the role of CNNs in computer vision and signal processingUnderstand the ethical implications of deep learning modelingUnderstand the mathematical terminology associated with deep learningCode a GAN and a VAE to generate images from a learned latent spaceImplement visualization techniques to compare AEs and VAEsWho this book is forThis book is for aspiring data scientists and deep learning engineers who want to get started with the fundamentals of deep learning and neural networks. Although no prior knowledge of deep learning or machine learning is required, familiarity with linear algebra and Python programming is necessary to get started. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88902931 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=520997
Titre : Deep Learning with R Cookbook Type de document : e-book Auteurs : Swarna GUPTA Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781789805673 Note générale : copyrighted Langues : Anglais (eng) Résumé : Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries Key Features Understand the intricacies of R deep learning packages to perform a range of deep learning tasks Implement deep learning techniques and algorithms for real-world use cases Explore various state-of-the-art techniques for fine-tuning neural network models Book Description Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques. The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You'll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you'll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you'll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps. By the end of this book, you'll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems. What you will learn Work with different datasets for image classification using CNNs Apply transfer learning to solve complex computer vision problems Use RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classification Implement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorization Build deep generative models to create photorealistic images using GANs and VAEs Use MXNet to accelerate the training of DL models through distributed computing Who this book is for This deep learning book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to learn key tasks in deep learning domains using a recipe-based approach. A strong understanding of machine learning and working knowledge of the R programming language is mandatory. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88882274 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=504336
Titre : Deep Reinforcement Learning Hands-On Type de document : e-book Auteurs : Maxim LAPAN Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781838826994 Note générale : copyrighted Langues : Anglais (eng) Résumé : New edition of the bestselling guide to deep reinforcement learning and how it's used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more Key Features Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods Apply RL methods to cheap hardware robotics platforms Book Description Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples. What you will learn Understand the deep learning context of RL and implement complex deep learning models Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others Build a practical hardware robot trained with RL methods for less than $100 Discover Microsoft's TextWorld environment, which is an interactive fiction games platform Use discrete optimization in RL to solve a Rubik's Cube Teach your agent to play Connect 4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI chatbots Discover advanced exploration techniques, including noisy networks and network distillation techniques Who this book is for Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88880129 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=501769 PermalinkPermalinkPermalinkDeveloping Multi-Platform Apps with Visual Studio Code / Ovais Mehboob Ahmed KHAN / PACKT PUBLISHING (2020)PermalinkPermalinkPermalinkDocker Certified Associate (DCA): Exam Guide / Francisco Javier Ramirez UREA / PACKT PUBLISHING (2020)PermalinkPermalinkPermalinkPermalinkExtending Microsoft Dynamics 365 Finance and Supply Chain Management Cookbook / Simon BUXTON / PACKT PUBLISHING (2020)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkHands-On Machine Learning with scikit-learn and Scientific Python Toolkits / Tarek AMR / PACKT PUBLISHING (2020)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkImplementing and Administering Cisco Solutions: 200-301 CCNA Exam Guide / Glen D. SINGH / PACKT PUBLISHING (2020)PermalinkImplementing Microsoft Azure Architect Technologies: AZ-303 Exam Prep and Beyond / Brett HARGREAVES / PACKT PUBLISHING (2020)PermalinkPermalinkImplementing Microsoft Dynamics 365 for Finance and Operations Apps / Jj YADAV / PACKT PUBLISHING (2020)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkLearn Quantum Computing with Python and IBM Quantum Experience / Robert LOREDO / PACKT PUBLISHING (2020)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalink
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