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Mayku FormBox Desktop Vacuum Forming Machine and Accessories - Mayku FBA180123UK, The MaykuFormBox puts the power of making in your hands and can turn your classroom into a creative powerhouse, giving students a hands-on STEM learning experience. Powered by any vacuum cleaner, it works with a range of materials and helps you to bring your creative ideas to life.Packing industrial strength into a small space, the desktop vacuum former is a powerful, portable learning tool. Create engagement in STEM curriculums and allow cost-effective exploration of product design, technology, manufacture and business concepts.Package includes:Mayku FormBoxUniversal adaptor to fit almost any vacuum cleaner20 transparent cast sheets20 white form sheets3 starter projects1kg of casting materialUK mains cableMayku 2 year extended warrantyHow it worksCreate - 3D print, sculpt or find your shape and place it on the FormBox bed.Form - The FormBox forms a sheet of material around your shape creating a mould in

Mayku FormBox Desktop Vacuum Forming Machine

Conrad Electronic

This wall clock with its face made of real slate brings a natural and simultaneously elegant flair to your home. By the unique grain of the rock, each clock is unique. The hour markings are discreetly engraved on the clock face to preserve the natural beauty of this wall clock. The original hands made of walnut wood are coherent. Even the heart of the watch is convincing due to its quality and sustainability. This energy-saving quality clockwork, with a deviation of less than 0.5 seconds per day, is much more accurate than other standard quartz clockwork. Due to the long running time, the battery needs to be replaced only about every three years. The hands slide almost silently over the clock face and do not interfere with the annoying ticking while sleeping, learning or reading.This text is machine translated.

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ENbook.pl

pstrongIntegrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problemsstrongppstrongKey Featuresstrongp ul liDelve into machine learning with this comprehensive guide to scikit-learn and scientific Pythonli liMaster the art of data-driven problem-solving with hands-on examplesli liFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsli ul pstrongBook Descriptionstrongp pMachine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.p pThe book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications.

Hands | On Machine Learning with scikit | learn and Scientific Python Toolkits | Amr Tarek | Twarda

A complete guide of theoretical, technical, and hands-on implementations for practical applications of machine learning across diverse domains in the industrybrpShows how data science and machine learning projects are executed in the real worldppProvides readers with the essential skills to tackle their own real-world problems with machine learning p

Practical Machine Learning with Python | Sarkar Dipanjan | Twarda

pstrongLearn how to solve real-world data problems using machine learning and RstrongppbrppstrongKey Features strongpulliThe 10th Anniversary Edition of the bestselling R machine learning book, updated with 50 new content for R 4.0.0 and beyondliliHarness the power of R to build flexible, effective, and transparent machine learning modelsliliLearn quickly with this clear, hands-on guide by machine learning expert Brett LantzliulpbrppstrongBook Description strongppMachine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.ppbrppMachine Learning with R, Fourth Edition provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need for data pre-processing, uncovering key insights, making new predictions, and v

Machine Learning with R | Fourth Edition | Lantz Brett | Twarda

pbGuides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming languagebp pMachine learning--a branch of Artificial Intelligence AI which enables computers to improve their results and learn new approaches without explicit instructions--allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. iPractical Machine Learning in Ri provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. p pBestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses o

Practical Machine Learning in R | Nwanganga Fred | Paperback | Twarda

pMachine Learning for Humans is an introduction to machine learning that is accessible to anyone with a basic understanding of high school mathematics. The book provides a practical understanding of machine learning through a hands-on approach, using Python as the programming language.pppbWhat you'll learn bppbrppulpp liThe fundamentals of machine learning, including supervised and unsupervised learning, and neural networks.lipp liHow to prepare and preprocess data for machine learning.lipp liHow to build, train, and evaluate machine learning models in Python.lipp liHow to use popular machine learning libraries, including scikit-learn and TensorFlow.lipp liHow to apply machine learning to real-world problems.lippulppbWho this book is for bppbrp pThis book is for anyone who wants to learn the basics of machine learning, including students, professionals, and hobbyists. It assumes no prior knowledge of machine learning or programming, although familiarity with Python is helpful.pppbWhy y

Machine Learning For Humans | Maini Vishal | Twarda

pstrongImplement supervised and unsupervised machine learning algorithms using C libraries such as PyTorch C API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasetsstrongppstrongKey Featuresstrongp ul liBecome familiar with data processing, performance measuring, and model selection using various C librariesli liImplement practical machine learning and deep learning techniques to build smart modelsli liDeploy machine learning models to work on mobile and embedded devicesli ul pstrongBook Descriptionstrongp pC can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning ML, showing you how to use C libraries to get the most out of your data. This book makes machine learning with C for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples.p pThis book will get you hands-

Hands | On Machine Learning with C++ | Kolodiazhnyi Kirill | Twarda

pFrom the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Bookstrong, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI and is the leader of a machine learning team at Gartner. This book is based on Andriy's own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders.ppbrppIf you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book.pp-Cassie Kozyrkov, Chief Decision Scientist at GoogleppbrppFoundational work about the reality of building machine learning models in production.pp-Karolis Urbonas, Head of Machine Learning and Science at Amazonp

Machine Learning Engineering | Burkov Andriy | Paperback | Twarda

pMachine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. p If you're familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You'll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. ul liLearn how to import, manipulate, and export data with H2O liExplore key machine-learning concepts, such as cross-validation and validation data sets liWork with three diverse data sets, including a regression, a multinomial classification, and a binomial cl

Practical Machine Learning with H2O | Cook Darren | Twarda

pIn Machine Learning Algorithms Handbook, Aman Kharwal, founder of Statso.io, takes you on an enlightening journey through the fascinating world of machine learning. Whether you are a seasoned data scientist or a curious beginner, this book provides a holistic overview of the essential algorithms that form the backbone of modern machine learning.ppbrppWith clarity and precision, Aman demystifies complex concepts, guiding you step-by-step through the fundamentals of regression, classification, clustering, deep learning, and time series forecasting. Each chapter presents a deep dive into a specific algorithm, equipping you with the knowledge and skills to tackle real-world problems head-on.ppbrppKey Features ppbrpp1. Clear Explanations of Machine Learning Algorithms The book offers clear and concise explanations of machine learning algorithms, ensuring that readers of all levels can grasp the concepts effortlessly.pp2. Hands-On Approach Packed with practical examples using Python and cod

Machine Learning Algorithms | Kharwal Aman | Twarda

pstrongSupercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environmentsstrongppbrppstrongKey Features strongpulliExplore hyperparameter optimization and model management toolsliliLearn object-oriented programming and functional programming in Python to build your own ML libraries and packagesliliExplore key ML engineering patterns like microservices and the Extract Transform Machine Learn ETML pattern with use casesliulpbrppstrongBook Description strongppMachine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services.ppbrppMachine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development method

Machine Learning Engineering with Python | McMahon Andrew P. | Twarda

pstrongGet to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologiesstrongppbrppstrongKey Features strongpulliGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choiceliliEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsliliFind out how you can make machine learning accessible for all users to promote decentralized processesliulpbrppstrongBook Description strongppEvery machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning AutoML is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort.ppbrppThis book reviews the underlying techniques of automated feature engineering, model and hy

Automated Machine Learning | Masood Adnan | Twarda

iMachine Learning in Financial Marketsi begins by structuring financial data in a way that is amenable to machine learning ML algorithms. Then, the author discusses how to conduct research with ML algorithms on that data and how to backtest your discoveries. Most of the problems and solutions are explained using math, supported by code. This makes the book very practical and hands-on. Readers become active users who can test the solutions proposed in their work. Readers will learn how to structure, label, weight, and backtest data. Machine learning is the future, and this book will equip investment professionals with the tools to utilize it moving forward.

Advances in Financial Machine Learning | Lopez de Prado Marcos | Twarda | Twarda

pstrongQuickly build and deploy massive data pipelines and improve productivity using Azure DatabricksstrongppbrppstrongKey Features strongpulliGet to grips with the distributed training and deployment of machine learning and deep learning modelsliliLearn how ETLs are integrated with Azure Data Factory and Delta LakeliliExplore deep learning and machine learning models in a distributed computing infrastructureliulpbrppstrongBook Description strongppMicrosoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines.ppbrppThe book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that wil

Distributed Data Systems with Azure Databricks | Palacio Alan Bernardo | Twarda

pAs data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially, however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilise machine learning effectively.ppAdopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders.ppDesigned as a guiding companion for both beginners and exp

Data Science and Machine Learning for Non | Programmers | Truong Dothang | Twarda

This book demonstrates various machine learning techniques and their implementation in JavaScript. Build models to power your applications with smart, predictive features. From predicting future prices, analyzing sentiments to medical diagnosis, this book shows you how to use the power of JavaScript to build efficient machine learning systems.

Hands | on Machine Learning with JavaScript | Kanber Burak | Paperback | Twarda

pTake a data-first and use-case driven approach to understanding machine learning and deep learning concepts with Low-Code AI. This hands-on guide presents three problem-focused ways to learn ML no code using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. You'll learn key ML concepts by using real-world datasets with realistic problems. p Business and data analysts get a project-based introduction to MLAI using a detailed, data-driven approach loading and analyzing data, feeding data into an ML model building, training, and testing and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications. p You'll learn how to ul liDistinguish structured and unstructured data and understand the different challenges they present liVisualize and analyze data liPreprocess data for input into a machine learning model liDiffe

Low | Code AI | Stripling Gwendolyn | Twarda

Brandosa

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pHow can music withstand the death and destruction brought on by war Global conflicts of the 20th century fundamentally transformed not only national boundaries, power relations, and global economies, but also the arts and culture of every nation involved. An important, unacknowledged aspect of these conflicts is that they have unique musical soundtracks. iMusic in World War IIi explores how music and sound took on radically different dimensions in the United States and Europe before, during, and after World War II. Additionally, the collection examines the impact of radio and film as the disseminators of the war's musical soundtrack. Contributors contend that the European and American soundtrack of World War II was largely one of escapism rather than the lofty, solemn, heroic, and celebratory mode of war music in the past. Furthermore, they explore the variety of experiences of populations forced from their homes and interned in civilian and POW camps in Europe and the United States, 

Music in World War II | Potter Pamela M. | Twarda

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Footway

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StreetStyle24

Spodnie męskie Lacoste Tracksuit Trousers Bottom XH9624-CCA – szare Męskie spodnie dresowe Lacoste Tracksuit Trousers Bottom. Zostały wykonane z wysokiej jakości materiału, który gwarantuje komfort użytkowania. Odzież posiada klasyczny krój z elastyczną, regulowaną za pomocą sznurka talią. Nogawki zakończone ściągaczem świetnie wyeksponują Twoje buty i zapewnią optymalne dopasowanie, a boczne kieszenie zmieszczą niezbędne drobiazgi. Męskie spodnie dresowe zadowolą fanów miejskiego stylu. Model ten świetnie dopełni męskie bluzy. W takim zestawie będziesz mógł wyruszyć na podbój miejskiej dżungli. Specyfikacja: - materiał: 84% bawełna, 16% poliester - klasyczny krój - elastyczna talia regulowana za pomocą sznurka - boczne kieszenie - nogawki zakończone ściągaczem - branding producentaPodmiot odpowiedzialny: Devanlay Polska Spółka z o. o. ul. Złota 59, 00-120 Warszawa, Polska

hands on machine learning