新书介绍 | 图算法指南,A Guide to Graph Algorithms 图论研究的是一种广泛的数学结构,用于刻画离散的对象及其之间的关系。而图算法则研究图论中计算问题的求解方法。图论和图算法在物理、化学、生物、社会科学等众多领域都发挥着重要作用。本书介绍图算法研究前沿领域,总结了近十年的进展。从图论概念、算法、问题模型...
图论研究的是一种广泛的数学结构,用于刻画离散的对象及其之间的关系。而图算法则研究图论中计算问题的求解方法。图论和图算法在物理、化学、生物、社会科学等众多领域都发挥着重要作用。本书介绍图算法研究前沿领域,总结了近十年的进展。从图论概念、算法、问题模型以及研究趋势等方面讨论了图算法研究领域的概貌和前沿。...
本书介绍图算法研究前沿领域。 图论研究的是一种广泛的数学结构,用于刻画离散的对象及其之间的关系。而图算法则研究图论中计算问题的求解方法。图论和图算法在物理、化学、生物、社会科学等众多领域都发挥着重要作用。本书介绍图算法研究前沿领域,总结了...
For multi-GPU, use a single graph across all GPUs. So far, this is only supported withthread-MPI, where the multi-GPU graph is defined by exploiting the natural ability of CUDA to fork and join streams across different GPUs within the same process (using event-based GPU-side synchronization...
A guide to machine learning algorithms and their applications The term ‘machine learning’ is often, incorrectly, interchanged with Artificial Intelligence[JB1] , but machine learning is actually a sub field/type of AI. Machine learning is also often referred to as predictive analytics, or ...
A deep-learning network trained on labeled data can then be applied to unstructured data, giving it access to much more input than machine-learning nets. This is a recipe for higher performance: the more data a net can train on, the more accurate it is likely to be. (Bad algorithms trai...
Learn how to use graph databases to solve real-world problems. This guide will explain the basics of graph databases, how they work, and the benefits they offer.
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps.
● Work with graph algorithms to solve problems like finding paths, centrality metrics, and detection of communities and clusters. ● Explore Neo4j’s GDS library through practical examples. ● Integrate machine learning with Neo4j graphs, covering data prep, feature extraction, and model training. ...
Graph databases use traversal algorithms to query the graph data model. Traversal algorithms may be depth-first or breadth-first, which helps to discover and retrieve connected data rapidly. Scalability Though it’s possible to scale a relation database horizontally (i.e., using sharding), it si...