Produkte zum Begriff Learning:
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Interface Design for Learning: Design Strategies for Learning Experiences
In offices, colleges, and living rooms across the globe, learners of all ages are logging into virtual laboratories, online classrooms, and 3D worlds. Kids from kindergarten to high school are honing math and literacy skills on their phones and iPads. If that weren’t enough, people worldwide are aggregating internet services (from social networks to media content) to learn from each other in “Personal Learning Environments.” Strange as it sounds, the future of education is now as much in the hands of digital designers and programmers as it is in the hands of teachers.And yet, as interface designers, how much do we really know about how people learn? How does interface design actually impact learning? And how do we design environments that support both the cognitive and emotional sides of learning experiences? The answers have been hidden away in the research on education, psychology, and human computer interaction, until now. Packed with over 100 evidence-based strategies, in this book you’ll learn how to:Design educational games, apps, and multimedia interfaces in ways that enhance learningSupport creativity, problem-solving, and collaboration through interface designDesign effective visual layouts, navigation, and multimedia for online and mobile learningImprove educational outcomes through interface design.
Preis: 18.18 € | Versand*: 0 € -
Interface Design for Learning: Design Strategies for Learning Experiences
In offices, colleges, and living rooms across the globe, learners of all ages are logging into virtual laboratories, online classrooms, and 3D worlds. Kids from kindergarten to high school are honing math and literacy skills on their phones and iPads. If that weren’t enough, people worldwide are aggregating internet services (from social networks to media content) to learn from each other in “Personal Learning Environments.” Strange as it sounds, the future of education is now as much in the hands of digital designers and programmers as it is in the hands of teachers.And yet, as interface designers, how much do we really know about how people learn? How does interface design actually impact learning? And how do we design environments that support both the cognitive and emotional sides of learning experiences? The answers have been hidden away in the research on education, psychology, and human computer interaction, until now. Packed with over 100 evidence-based strategies, in this book you’ll learn how to:Design educational games, apps, and multimedia interfaces in ways that enhance learningSupport creativity, problem-solving, and collaboration through interface designDesign effective visual layouts, navigation, and multimedia for online and mobile learningImprove educational outcomes through interface design.
Preis: 24.6 € | Versand*: 0 € -
Deep Learning Design Patterns
Deep learning has revealed ways to create algorithms for applications that we never dreamed were possible. For software developers, the challenge lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Design Patterns is here to help. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Written by Google deep learning expert Andrew Ferlitsch, it's filled with the latest deep learning insights and best practices from his work with Google Cloud AI. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples.about the technologyYou don't need to design your deep learning applications from scratch! By viewing cutting-edge deep learning models as design patterns, developers can speed up their creation of AI models and improve model understandability for both themselves and other users.about the bookDeep Learning Design Patterns distills models from the latest research papers into practical design patterns applicable to enterprise AI projects. Using diagrams, code samples, and easy-to-understand language, Google Cloud AI expert Andrew Ferlitsch shares insights from state-of-the-art neural networks. You'll learn how to integrate design patterns into deep learning systems from some amazing examples, including a real-estate program that can evaluate house prices just from uploaded photos and a speaking AI capable of delivering live sports broadcasting. Building on your existing deep learning knowledge, you'll quickly learn to incorporate the very latest models and techniques into your apps as idiomatic, composable, and reusable design patterns. what's insideInternal functioning of modern convolutional neural networksProcedural reuse design pattern for CNN architecturesModels for mobile and IoT devicesComposable design pattern for automatic learning methodsAssembling large-scale model deploymentsComplete code samples and example notebooksAccompanying YouTube videosabout the readerFor machine learning engineers familiar with Python and deep learning.about the authorAndrew Ferlitsch is an expert on computer vision and deep learning at Google Cloud AI Developer Relations. He was formerly a principal research scientist for 20 years at Sharp Corporation of Japan, where he amassed 115 US patents and worked on emerging technologies in telepresence, augmented reality, digital signage, and autonomous vehicles. In his present role, he reaches out to developer communities, corporations and universities, teaching deep learning and evangelizing Google's AI technologies.
Preis: 58.84 € | Versand*: 0 € -
Ekman, Magnus: Learning Deep Learning
Learning Deep Learning , NVIDIA's Full-Color Guide to Deep Learning: All StudentsNeed to Get Started and Get Results Learning Deep Learning is a complete guide to DL.Illuminating both the core concepts and the hands-on programming techniquesneeded to succeed, this book suits seasoned developers, data scientists,analysts, but also those with no prior machine learning or statisticsexperience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers,Magnus Ekman shows how to use them to build advanced architectures, includingthe Transformer. He describes how these concepts are used to build modernnetworks for computer vision and natural language processing (NLP), includingMask R-CNN, GPT, and BERT. And he explains how a natural language translatorand a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples usingTensorFlow with Keras. Corresponding PyTorch examples are provided online, andthe book thereby covers the two dominating Python libraries for DL used inindustry and academia. He concludes with an introduction to neural architecturesearch (NAS), exploring important ethical issues and providing resources forfurther learning. Exploreand master core concepts: perceptrons, gradient-based learning, sigmoidneurons, and back propagation See how DL frameworks make it easier to developmore complicated and useful neural networks Discover how convolutional neuralnetworks (CNNs) revolutionize image classification and analysis Apply recurrentneural networks (RNNs) and long short-term memory (LSTM) to text and othervariable-length sequences Master NLP with sequence-to-sequence networks and theTransformer architecture Build applications for natural language translation andimage captioning , >
Preis: 49.28 € | Versand*: 0 €
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Warum Deep Learning im Vergleich zu Machine Learning?
Deep Learning unterscheidet sich von Machine Learning durch seine Fähigkeit, automatisch Merkmale aus den Daten zu extrahieren, anstatt dass diese manuell definiert werden müssen. Dadurch ist Deep Learning in der Lage, komplexere und abstraktere Muster in den Daten zu erkennen und zu lernen. Dies ermöglicht es Deep Learning-Modellen, in vielen Anwendungsbereichen, wie Bild- und Spracherkennung, bessere Leistungen zu erzielen als herkömmliche Machine Learning-Modelle.
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Was ist der Unterschied zwischen Deep Learning und Machine Learning?
Deep Learning ist eine spezielle Methode des Machine Learning, die auf künstlichen neuronalen Netzwerken basiert. Es ermöglicht das Lernen von hierarchischen und komplexen Merkmalsdarstellungen, um automatisch Muster und Strukturen in Daten zu erkennen. Im Gegensatz dazu ist Machine Learning ein breiterer Begriff, der verschiedene Algorithmen und Techniken umfasst, um Computermodelle zu erstellen, die aus Daten lernen und Vorhersagen treffen können. Deep Learning ist also eine Teilmenge des Machine Learning.
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Wie kann 3D-Modellierung dazu beitragen, realistische Darstellungen von Objekten zu erstellen? Welche Programme und Techniken werden zur 3D-Modellierung von Architektur und Design verwendet?
3D-Modellierung ermöglicht es, detaillierte und realistische Darstellungen von Objekten zu erstellen, indem sie Dimensionen, Texturen und Beleuchtung simuliert. Für die 3D-Modellierung von Architektur und Design werden Programme wie Autodesk Revit, SketchUp, Blender und Cinema 4D verwendet. Techniken wie Polygonmodellierung, NURBS-Modellierung und Voxelmodellierung werden eingesetzt, um komplexe Strukturen und Formen zu erstellen.
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Was ist Python Machine Learning?
Python Machine Learning bezieht sich auf die Verwendung von Python-Programmierung, um maschinelles Lernen zu implementieren. Dabei werden Algorithmen und Modelle erstellt, die es Computern ermöglichen, aus Daten zu lernen und Vorhersagen zu treffen. Python bietet eine Vielzahl von Bibliotheken wie Scikit-learn, TensorFlow und Keras, die das Entwickeln von Machine-Learning-Anwendungen erleichtern. Mit Python Machine Learning können komplexe Probleme gelöst und Muster in großen Datenmengen entdeckt werden.
Ähnliche Suchbegriffe für Learning:
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Architektur und Computer - Planung und Konstruktion im digitalen Zeitalter
Virtuelle Architektur! Computer bestimmen immer mehr den Alltag des Architekten - mittlerweile gibt es eine ganze Reihe von Programmen, die Gebäude nur auf dem Bildschirm entstehen lassen können. Dadurch eröffnet sich eine Vielzahl neuer Möglichkeiten für künftige Bau-Projekte. Frank O. Gehrys Guggenheim-Museum in Bilbao ist das prägnanteste Beispiel dafür, daß Planung und Konstruktion ohne den Einsatz von Software nicht möglich gewesen wären. James Steele präsentiert in diesem Buch die spektakulärsten öffentlichen Gebäude und Entwürfe der letzten Jahre und erläutert ihre Entstehungsgeschichte. Eine grundlegende und notwendige Diskussion über den Einsatz des Computers in der Architektur!
Preis: 74.90 € | Versand*: 6.95 € -
Easy Learning
Kinder-Wanduhr "Easy Learning", Durchmesser 30 cm, geräuscharm
Preis: 25.49 € | Versand*: 6.95 € -
Easy Learning
Kinder-Wanduhr "Easy Learning", Durchmesser 30 cm, geräuscharm
Preis: 23.96 € | Versand*: 6.96 € -
Learning Blender
Master the Newest Blender Techniques for Creating Amazing 3D Characters: From Design and Modeling to Video CompositingNow fully updated for Blender 2.83 LTS (Long-Term Support) and beyond, Learning Blender, Third Edition, walks you through every step of creating an outstanding 3D animated character with Blender, and then compositing it in a real video using a professional workflow. This edition covers the extensive interface changes of the software, as well as many improvements and some almost fully rewritten chapters to showcase more modern workflows.Still the only Blender tutorial to take you from preproduction to final result, this guide is perfect for both novices and those moving from other software to Blender (open source and free software). Author Oliver Villar provides full-color, hands-on chapters that cover every aspect of character creation: design, modeling, unwrapping, texturing, shading, rigging, animation, and rendering. He also walks you through integrating your animated character into a real-world video, using professional camera tracking, lighting, and compositing techniques.The rich companion website (blendtuts.com/learning-blender-files) will help you quickly master even the most complex techniques with bonus contents like video tutorials. By the time you're done, you'll be ready to create outstanding characters for all media -- and you'll have up-to-date skills for any 3D project, whether it involves characters or not.Learn Blender's updated user interface and navigationCreate your first scene with Blender and the Blender Render and Cycles render enginesOrganize an efficient, step-by-step pipeline to streamline workflow in any projectMaster modeling, unwrapping, and texturingBring your character to life with materials and shading in both Cycles and EEVEE (the new real-time render engine included in Blender)Create your character's skeleton and make it walkUse Camera Tracking to mix 3D objects into a real-world videoTransform a raw rendered scene into the final result using Blender's compositing nodesRegister your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Preis: 33.16 € | Versand*: 0 €
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Ist Machine Learning bereits künstliche Intelligenz?
Machine Learning ist ein Teilgebiet der künstlichen Intelligenz. Es befasst sich mit der Entwicklung von Algorithmen und Modellen, die es Computern ermöglichen, aus Daten zu lernen und Vorhersagen zu treffen. Künstliche Intelligenz umfasst jedoch auch andere Bereiche wie Expertensysteme, natürliche Sprachverarbeitung und Robotik.
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Was ist eure Meinung zum Distance Learning?
Als KI kann ich keine Meinung haben, aber Distance Learning hat Vor- und Nachteile. Einerseits ermöglicht es den Zugang zu Bildung für Menschen, die keine Möglichkeit haben, physisch an einer Schule oder Universität zu sein. Andererseits kann es schwierig sein, die gleiche Qualität der Bildung und den persönlichen Austausch zu erreichen, der in einem traditionellen Klassenzimmer stattfindet.
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Ist ein Machine Learning Engineer ein Ingenieur?
Ja, ein Machine Learning Engineer ist ein Ingenieur. Sie haben in der Regel einen technischen Hintergrund und arbeiten an der Entwicklung und Implementierung von Machine Learning-Modellen und -Algorithmen. Sie nutzen ihre technischen Fähigkeiten, um Daten zu analysieren, Modelle zu trainieren und Lösungen für komplexe Probleme zu entwickeln.
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Wann verwendet man "studying" und wann "learning"?
"Studying" wird normalerweise verwendet, um sich auf das formelle Lernen in einer akademischen Umgebung zu beziehen, wie zum Beispiel das Lesen von Büchern oder das Besuchen von Vorlesungen. "Learning" hingegen kann sich auf jegliche Art von Wissenserwerb beziehen, sei es durch formales Lernen oder durch informelles Lernen im Alltag. Es kann auch verwendet werden, um auf den Prozess des Verstehens und Absorbierens von Informationen im Allgemeinen hinzuweisen.
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