William L. Hamilton loves a good gimlet. Rose's and lime. Straight up. Perfectly iced. Make the glass pretty too. "It ruined my reputation for thinking before I speak," he writes of that love. "I accept the trade-off." Like Lewis Carroll's Alice, when Hamilton sees it, he drinks it -- and tells the incredible tale.
In "Shaken and Stirred," his biweekly Sunday Styles column, now an original book of his drinking adventures, the intrepid New York Times reporter offers a gimlet-eyed look at contemporary culture through the panoptic view of a cocktail glass. From the venerable martini to the young Dirty Jane, Hamilton shares his tip on the sip.
You hold in your hands a guide to "how it goes down." Not a cocktail manual or a Baedeker to the bar scene but a drinker's guide to drinking. These are four-ounce adventures of cocktails and the people who make them, from the bartenders and chefs to the patrons, the politicians and the power players of the liquor industry.
There are tales of the Champagne high life, the Long Island Iced Tea low life; men like Dr. Brown and his celery soda, and women like Eve and her Apple Martini. Hamilton's weekly Runyanesque rounds cover all the watering holes and their poisons, from the East Side's Southside to the Incredible Hulk in the Bronx, and monitors the latest trends, from the ultra-premium vodka wars to the Red Bull market. Shaken and Stirred is a report on a popular culture that comes alive after five, when the mood turns social and the moment is sweet (or sour, or bitter, or dry).
Hamilton has also picked up the best (or the most unbelievable) cocktail recipes from bars, lounges and restaurants in New York City and beyond. There is common sense and creativity in the classics, and new inventions with their eye on the prize, such as the Huckleberry Ginn and the Bleeding Heart."drink me," said the bottle in Alice's Adventures in Wonderland. Hamilton has, in every instance, and bottled his thoughts in sixty-four essays that are as readable as they are drinkable. Mix a gimlet, or a Minnesota Anti-Freeze, or a Gibson or a Bone. And spend a night in, on the town.

Shaken and Stirred: Through the Martini Glass and Other Drinking Adventures

Hamilton, William L.

Reference

William Morrow

There are more psychopaths pretending to be saints than there are real saints. This book will provide you with some understanding of what a psychopath is, what a saint is and how to tell the difference. It is also a sharing of what Mr. Hamilton found in his own search for inner peace and ways to develop his unrealized potential. He provides a candid view of his experiences with a psychopathic teacher and a psychopathic wife which richly illustrate the type of behavior we should all watch out for. 
This book is more than just a warning to watch out for false spiritual teachers. Mr. Hamilton draws on the experience of the Snowmass Contemplative Group which is a group of advanced contemplatives from many different religious traditions. They have developed an eclectic understanding of enlightenment from all religious traditions. Mr. Hamilton extends and refines this view with his knowledge of Buddhist teachings which he believes provide the most detailed understanding of enlightenment. He cites scientific studies of enlightenment which takes it beyond the realm of mystical experience and develops it into a Western psychological concept.

Saints and Psychopaths

Religion & Spirituality

Dana

The theme of the book is that the world of the spirit is real and can be learned, to some extent at least. And that spiritual kinowledge based on fact is truly power, and it brings satisfaction and contentment beyond all else to those who seek it. It is about the teaching of "The Ancient Wisdom". Jesus came to teach this as did others before him and others after. There is nothing new about the basic facts. What is new is the more effective way that it can be explained in this modern era. There is no plot---just 50 essays, most of them based on spiritual experiences of the author, each one illustrating some aspect of the world of the spirit. Simple enough for the beginner to get started, yet deep enough to interest for a lifetime. Some aspects are approached from more than one perspective for clarity, inluding metaphysics and a New Testament outlook with remarkable conclusions. Not totally a how-to book, rather a way to select appropriate how-to books. Crammed with valuable insights, it can be a captivating guide for anyone seeking the ancient eternal wisdom. The author, a mechanical engineer, began a spiritual quest at age 37. Adept in the world of science and machines, he was an unlikely personality type to get very far in this type of endeavor. But it soon became apparent to him that there is no real conflict between proven science and true religion. Although this realization is contrary to some long-held popular beliefs, it simplified his religious studies immensely. He met some qualified teachers who knew that the great religious truths are meant for 'here and now' in this lifetime as well as any afterlife, and could teach it well. The eternal wisdom (some of it anyway) 'came through' even to the literal scientific mind of the engineer. And (some of it anyway) can be passed on to the reader.

The Spiritual Quest of a Scientific Mind: An engineer's search for meaning beyond science

CreateSpace Independent Publishing Platform

Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning, 46)

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.

Computers & Technology

Computer Science

Morgan Claypool

This book is a foundational guide to graph representation learning, including state-of-the art advances, and introduces the highly successful graph neural network (GNN) formalism. 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. 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.

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.

Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Springer

See the best price to sell, buy, or rent books by Hamilton, William L.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Hamilton, William L.

See the best price to sell, buy, or rent books by Hamilton, William L.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Shaken and Stirred: Through the Martini Glass and Other Drinking Adventures

Saints and Psychopaths

The Spiritual Quest of a Scientific Mind: An engineer's search for meaning beyond science

Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning, 46)

Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning, 46)

Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)