Es wurden 59 Produkte zu dem Suchbegriff representation learning in 13 Shops gefunden:

The Learning Journey: Leuchtendes Piratenschiff-Puzzle ist ein spannendes und lehrreiches Spiel, das die Fantasie und Problemlösungsfähigkeiten der Kinder stärkt. Produkteigenschaften: 100-teilige Puzzle aus dickem und haltbarem Karton Tagsüber zeigt es eine Piratenszene, nachts leuchtet über 30% im Dunkeln Fördert taktile, motorische, denk- und lernfähigkeiten Ermutigt zur Erkennung von Farben, Formen und Objekten Alle Produkte werden streng auf Sicherheit getestet

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

AK Learning: Painting Female Figures von AK-Interactive AK Learning 12 : Painting Female Figures Die 12. Ausgabe einer der berühmtesten Sagen in der Welt des Modellbaus. Entdecken Sie die Besonderheiten der weiblichen Figurenmalerei. Einige der besten Figurenmaler zeigen uns auf einfache Weise ihre Tricks und Techniken, die Ihnen helfen, das Malen weiblicher Miniaturen zu meistern. Holen Sie das Maximum an Realismus und Schönheit in jede Figur, egal ob Sie Anfänger oder fortgeschrittener Maler sind. Dieses Buch ist üppig illustriert mit Schritt-für-Schritt-Fotos und anschaulichem Text, der die Geheimnisse und Methoden der Figurenmalerei ausführlich erklärt. Auf diesen Seiten finden Sie interessante und sehr nützliche Informationen für Ihre Projekte mit Figuren in jedem Maßstab. Englisch, 96 Seiten. Softcover.

This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book. In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques’ effectiveness.

Microsoft Viva Learning (NCE)

This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book. In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques’ effectiveness.

Microsoft Viva Learning (NCE)

"The World as Will and Representation" is a work by the outstanding German philosopher Arthur Schopenhauer (German: Arthur Schopenhauer, 1788-1860). Human nature is a source of suffering, since at the basis of any action lies an unquenchable thirst for life. Great passions give rise to great languor. Other works by Schopenhauer include "On the Will in Nature", "On the Freedom of the Will", "On the Basis of Morality", "The Two Fundamental Problems of Ethics" and "On the Fourfold Root of the Principle of Sufficient Reason." Arthur Schopenhauer was called a "pessimistic philosopher": he believed the existing world to be "the worst possible," in contrast to the German thinker Gottfried Leibniz.

SparkFun MicroMod Machine Learning Carrier Board

This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions.

Vorbereitung erleichtert das Lernen Reinheit bei Welpen.

This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions.

Tastatur designt für Kinder / Zwei integrierte USB 2.0-Hochgeschwindigkeits-Hubs / Kabelgebundene USB-Verbindung / Abmessungen: 446mm x 141mm x 23mm (17,6"x 5,6"x 0,9")

This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions.

Entdecken Sie die ICE learning, eine Uhr, die entwickelt wurde, um das Lernen der Uhrzeit für Kinder einfach und unterhaltsam zu machen. Diese Uhr ist ein zauberhafter Begleiter für ihren Träger und gleichzeitig ein wichtiges Lernmittel. Die ICE learning ist leicht zu lesen und liebenswert zu tragen und begleitet Dein Kind beim täglichen Lernen auf fröhliche und farbenfrohe Weise., Produktdetails: Modellbezeichnung: 024502, Funktionen: Antrieb: Quarz, Anzeige: analog, Wasserdicht: bis 3 bar, Farbe: Zifferblattfarbe: rot, Gehäusefarbe: blau, Armbandfarbe: rot, Material: Gehäusematerial: Kunststoff, Armbandmaterial: Nylon, Armband: Verschluss Armband: Dornschließe, Maßangaben: Gehäusedurchmesser (ohne Krone): 28 mm, Stromversorgung: Anzahl Batterien: 1 Stk., Batterie-/Akku-Technologie: SR621SW, Details: Besondere Merkmale: Armbanduhr,Kinderuhr,Lernuhr,Jungen,Schulanfang,Geschenkidee,Fußball, Technische Daten: WEEE-Reg.-Nr. DE: 80.617.385

The topic of this book is mental representation, a theoretical concept that lies at the core of cognitive science. Together with the idea that thinking is analogous to computational processing, this concept is responsible for the "cognitive turn" in the sciences of the mind and brain since the 1950s. Conceiving of cognitive processes (such as perception, reasoning, and motor control) as consisting of the manipulation of contentful vehicles that represent the world has led to tremendous empirical advancements in our explanations of behaviour. Perhaps the most famous discovery that explains behavior by appealing to the notion of mental representations was the discovery of 'place' cells that underlie spatial navigation and positioning, which earned researchers John O'Keefe, May-Britt Moser, and Edvard I. Moser a joint Nobel Prize in 2014. And yet, despite the empirical importance of the concept, there is no agreed definition or theoretical understanding of mental representation. This book constitutes a state-of-the-art overview on the topic of mental representation, assembling some of the leading experts in the field and allowing them to engage in meaningful exchanges over some of the most contentious questions. The collection gathers both proponents and critics of the notion, making room for debates dealing with the theoretical and ontological status of representations, the possibility of formulating a general account of mental representation which would fit our best explanatory practices, and the possibility of delivering such an account in fully naturalistic terms. Some contributors explore the relation between mutually incompatible notions of mental representation, stemming from the different disciplines composing the cognitive sciences (such as neuroscience, psychology, and computer science). Others question the ontological status and explanatory usefulness of the notion. And finally, some try to sketch a general theory of mental representations that could face the challenges outlined in the more critical chapters of the volume.

"The Learning Journey: Mein erstes Puzzle - Alphabet" ist das perfekte Einsteiger-Puzzle-Set für Kleinkinder, das die Feinmotorik fördert. Produkteigenschaften: 4 Puzzles in einer Box 2-teiliges, 4-teiliges, 6-teiliges und 8-teiliges Puzzle Große, dicke Teile für kleine Hände Fördert die taktile/motorische Fähigkeiten und die Hand-Auge-Koordination Langlebige Kartonkonstruktion Puzzlegröße: 31,75 cm x 21,6 cm

This book introduces approaches to generalize the benefits of equivariant deep learning to a broader set of learned structures through learned homomorphisms. In the field of machine learning, the idea of incorporating knowledge of data symmetries into artificial neural networks is known as equivariant deep learning and has led to the development of cutting edge architectures for image and physical data processing. The power of these models originates from data-specific structures ingrained in them through careful engineering. To-date however, the ability for practitioners to build such a structure into models is limited to situations where the data must exactly obey specific mathematical symmetries. The authors discuss naturally inspired inductive biases, specifically those which may provide types of efficiency and generalization benefits through what are known as homomorphic representations, a new general type of structured representation inspired from techniques in physics and neuroscience. A review of some of the first attempts at building models with learned homomorphic representations are introduced. The authors demonstrate that these inductive biases improve the ability of models to represent natural transformations and ultimately pave the way to the future of efficient and effective artificial neural networks.

AK Learning Wargames Series 2: Starship Techniques von AK-Interactive Bitte beachten, dass es sich um ein Produkt in englischer Sprache handelt. The way wargame painters approach their projects is different from other fields, such as historic model making. This collection Learning Wargames consists of several volumes for wargaming enthusiasts, both for those who want to make the leap to the tabletop profile, as well as those who are looking to further improve their painting or to make showpieces. Each book focuses on a specific theme and provides the reader with a wide range of knowledge and techniques to help their skills. This second volume is dedicated to ships with complex finishes.

This book introduces approaches to generalize the benefits of equivariant deep learning to a broader set of learned structures through learned homomorphisms. In the field of machine learning, the idea of incorporating knowledge of data symmetries into artificial neural networks is known as equivariant deep learning and has led to the development of cutting edge architectures for image and physical data processing. The power of these models originates from data-specific structures ingrained in them through careful engineering. To-date however, the ability for practitioners to build such a structure into models is limited to situations where the data must exactly obey specific mathematical symmetries. The authors discuss naturally inspired inductive biases, specifically those which may provide types of efficiency and generalization benefits through what are known as homomorphic representations, a new general type of structured representation inspired from techniques in physics and neuroscience. A review of some of the first attempts at building models with learned homomorphic representations are introduced. The authors demonstrate that these inductive biases improve the ability of models to represent natural transformations and ultimately pave the way to the future of efficient and effective artificial neural networks.