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Every year graduating engineers are told that they are destined for success. But what are the habits and behaviours that actually lead to success? In What I did not learn at IIT, Rajeev Agarwal, founder and CEO of MAQ Software, has distilled decades of life experience into one accessible and informative guide. In simple language, he explains the success techniques he applied and what worked for him. Encouraging graduates to look at their careers over a forty-year span, Rajeev explains that successful people choose to be passionate about every job they have. Using a skillful combination of personal stories and checklists, What I did not learn at IIT provides students-young and old-with a roadmap for success.
Every year, top performers join management ranks in their companies. As they assume their new roles, managers often receive inadequate training on adopting the right mindsets and behaviors to succeed. Combining his experience as the founder and CEO of MAQ Software, with research by leading management thinkers such as Peter Drucker and Henry Mintzberg, Rajeev Agarwal offers insights on key issues faced by managers, including motivating team members, what a manager does and why they are so busy, how to delegate, train a team, provide feedback, retain employees and whether pay matters Whether you are a recent MBA graduate or an aspiring manager, What I Did Not Learn in B-School provides useful tools to set you on the path to managerial success.
The first survey of the classic twentieth-century houses that defined American Midwestern modernism. Famed as the birthplace of that icon of twentieth-century architecture, the skyscraper, Chicago also cultivated a more humble but no less consequential form of modernism--the private residence. Modern in the Middle: Chicago Houses 1929-75 explores the substantial yet overlooked role that Chicago and its suburbs played in the development of the modern single-family house in the twentieth century. In a city often associated with the outsize reputations of Frank Lloyd Wright and Ludwig Mies van der Rohe, the examples discussed in this generously illustrated book expand and enrich the story of the region's built environment. Authors Susan Benjamin and Michelangelo Sabatino survey dozens of influential houses by architects whose contributions are ripe for reappraisal, such as Paul Schweikher, Harry Weese, Keck & Keck, and William Pereira. From the bold, early example of the "Battledeck House" by Henry Dubin (1930) to John Vinci and Lawrence Kenny's gem the Freeark House (1975), the generation-spanning residences discussed here reveal how these architects contended with climate and natural setting while negotiating the dominant influences of Wright and Mies. They also reveal how residential clients--typically middle-class professionals, progressive in their thinking--helped to trailblaze modern architecture in America. Though reflecting different approaches to site, space, structure, and materials, the examples in Modern in the Middle reveal an abundance of astonishing houses that have never been collected into one study--until now.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
THE TEACHING APTITUDE MCQ (MULTIPLE CHOICE QUESTIONS) SERVES AS A VALUABLE RESOURCE FOR INDIVIDUALS AIMING TO DEEPEN THEIR UNDERSTANDING OF VARIOUS COMPETITIVE EXAMS, CLASS TESTS, QUIZ COMPETITIONS, AND SIMILAR ASSESSMENTS. WITH ITS EXTENSIVE COLLECTION OF MCQS, THIS BOOK EMPOWERS YOU TO ASSESS YOUR GRASP OF THE SUBJECT MATTER AND YOUR PROFICIENCY LEVEL. BY ENGAGING WITH THESE MULTIPLE-CHOICE QUESTIONS, YOU CAN IMPROVE YOUR KNOWLEDGE OF THE SUBJECT, IDENTIFY AREAS FOR IMPROVEMENT, AND LAY A SOLID FOUNDATION. DIVE INTO THE TEACHING APTITUDE MCQ TO EXPAND YOUR TEACHING APTITUDE KNOWLEDGE AND EXCEL IN QUIZ COMPETITIONS, ACADEMIC STUDIES, OR PROFESSIONAL ENDEAVORS. THE ANSWERS TO THE QUESTIONS ARE PROVIDED AT THE END OF EACH PAGE, MAKING IT EASY FOR PARTICIPANTS TO VERIFY THEIR ANSWERS AND PREPARE EFFECTIVELY.
Designed for a one-semester course in Finite Element Method, this compact and well-organized text presents FEM as a tool to find approximate solutions to differential equations. This provides the student a better perspective on the technique and its wide range of applications. This approach reflects the current trend as the present-day applications range from structures to biomechanics to electromagnetics, unlike in conventional texts that view FEM primarily as an extension of matrix methods of structural analysis. After an introduction and a review of mathematical preliminaries, the book gives a detailed discussion on FEM as a technique for solving differential equations and variational formulation of FEM. This is followed by a lucid presentation of one-dimensional and two-dimensional finite elements and finite element formulation for dynamics. The book concludes with some case studies that focus on industrial problems and Appendices that include mini-project topics based on near-real-life problems. Postgraduate/Senior undergraduate students of civil, mechanical and aeronautical engineering will find this text extremely useful; it will also appeal to the practising engineers and the teaching community.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
This book is a collection of chapters reflecting the experiences and achievements of some of the Fellows of the Indian National Academy of Engineering (INAE). The book comprises essays that look at reminiscences, eureka moments, inspirations, challenges and opportunities in the journey of an engineering professional. The chapters look at the paths successful engineering professionals take towards self-realisation, the milestones they crossed, and the goals they reached. The book contains 38 chapters on diverse topics that truly reflect the way the meaningful mind of an engineer works.
The next book in the Cal Leandros series that began with Nightlife, Madhouse is a clever and hugely entertaining urban fantasy in the vein of Jim Butcher by New York Times bestselling author, Rob Thurman. 'My brother had spent a lifetime - mine, at least - telling me that I was normal, that I wasn't a monster. With his help, I'd finally realized that as long as I could remain who I was, I could survive what I was. It was only bad genes . . .' Half-human Cal Leandros and his brother, Niko, aren't exactly prospering with their preternatural detective agency. Who could have guessed that business would dry up in New York City, where vampires, trolls, and other creepy crawlies are all over the place? But now there's a new arrival in the Big Apple. A malevolent evil with ancient powers, dead set on making history with an orgy of blood and murder, is on a human killing spree. And for Cal and Niko, this is one paycheck they're going to have to earn . . . if they survive to collect it. But this evil might be too much for even Cal and Niko to handle, and to take it on, Cal will have to call forth the powerful talents he inherited from his unhuman father's side of the family . . . A stunning urban fantasy with a deeply humorous edge, Rob Thurman's Madhouse is not to be missed. 'Supernatural elements put this in the company of Jim Butcher and Charlaine Harris. Fans of dark urban fantasy will certainly want to follow Cal's adventures. Recommended' SFRevu 'A roaring rollercoaster of a read . . . it's sharp and sardonic, mischievous and mysterious' Simon R. Green Rob Thurman lives in Indiana, land of rolling hills and cows. Lots and lots of cows. Nightlife, the first novel in the Cal Leandros series is also published by Penguin. Visit Rob at www.robthurman.net