The Opnet User Guide for Practical Computer Network Simulation

Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting se

Practical General Practice is a highly practical manual, specifically designed for use during the consultation process. Containing over 1000 conditions, the unique underlying structure of the book allows the GP to see immediately what treatment is recommended and why. All recommendations are highly specific - giving a firm guide to the GP during the consultation process rather than a list of possibilities that the GP might wish to consider.Bullet points for action which give the GP an immediate summary of the issues that must be covered in the consultation. Bullet points of the key evidence which justifies those recommendations. All chapters thoroughly revised, to reflect changes in the evidence, and in major guidelines, since the last edition. NNT values provided when available