This book is about the types of new roles we need to play in our fast-changing technology-oriented world so that we are truly tech-proof. It provides readers information and observations on a variety of technology-related subjects so that they are able to pivot on a dime when they need to. This is the ultimate guide that will help readers remain relevant in the rapidly evolving world of technology.
This book is about the types of new roles we need to play in our fast-changing technology-oriented world so that we are truly tech-proof. It provides readers information and observations on a variety of technology-related subjects so that they are able to pivot on a dime when they need to. This is the ultimate guide that will help readers remain relevant in the rapidly evolving world of technology.
This book contains 50 sample papers with answers which are based on latest exam pattern given by CLAT Consortium. This books also contains previous year solved paper.
This key text addresses the complex computer chips of tomorrow which will consist of several layers of metal interconnect, making the interconnect within a chip or a multichip module a three dimensional problem. You'll find an insightful approach to the algorithmic, cell design issues in chip and MCM routing with an emphasis on techniques for eliminating routing area.
Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. Learn from 13 practical case studies using field, laboratory, and simulation data Become knowledgeable with data science and analytics terminology relevant to subsurface characterization Learn frameworks, concepts, and methods important for the engineer’s and geoscientist’s toolbox needed to support
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