Rényi Picture Dictionary PDF Download
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Author:
Publisher: Editions Renyi
ISBN: 9780921606475
Category : English language
Languages : en
Pages : 190
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Book Description
Author:
Publisher:
ISBN:
Category : Italian language
Languages : en
Pages : 0
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Book Description
Author:
Publisher: Editions Renyi
ISBN: 9780921606475
Category : English language
Languages : en
Pages : 190
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Book Description
Author: Kingsmill Editions, Incorporated
Publisher: Editions Renyi
ISBN: 9780921606123
Category : Foreign Language Study
Languages : hy
Pages : 194
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Book Description
Author:
Publisher: ROHAN PRAKASHAN
ISBN:
Category :
Languages : en
Pages : 55
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Author:
Publisher:
ISBN:
Category : Picture dictionaries, Polish
Languages : en
Pages :
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Book Description
Author: P. O'Brien-Hitching
Publisher: Langenscheidt Pub Incorporated
ISBN: 9780887298516
Category : Juvenile Nonfiction
Languages : en
Pages : 180
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Book Description
Labeled drawings illustrate the meaning of French words and phrases dealing with people, animals, objects, and actions.
Author:
Publisher:
ISBN:
Category : English language
Languages : en
Pages : 0
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Book Description
An illustrated Japanese/English dictionary, each picture being accompanied by a single word or a sentence in each language.
Author: William L. William L. Hamilton
Publisher: Springer Nature
ISBN: 3031015886
Category : Computers
Languages : en
Pages : 141
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Book Description
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.
Author:
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ISBN:
Category : Italian language
Languages : en
Pages : 0
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Book Description
Author: Avrim Blum
Publisher: Cambridge University Press
ISBN: 1108617360
Category : Computers
Languages : en
Pages : 433
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Book Description
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
