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'algoritmus' címkével ellátott könyvek a rukkolán

 


Katona Gyula Y. - Recski András - Szabó Csaba - Gráfelmélet, ​algoritmuselmélet és algebra
Ehhez a könyvhöz nincs fülszöveg, de ettől függetlenül még rukkolható/happolható.

Hornig Trapp Weltner - További ​tippek és trükkök a Commodore 64-eshez
A ​könyvben ismertetett kapcsolások, eljárások és programok nem tekinthetők szabadalmi oltalom alá eső ipari termékeknek. Ezek elsősorban amatőr és oktatási célokat szolgálnak. A szerzők rendkívül nagy gondot fordítottak a kapcsolások, műszaki adatok és programok helyességére, a részletek kidolgozása során többszöri ellenőrzést végeztek. Mindez azonban nem zárja ki az esetleges hibalehetőségeket. Az előforduló hibákért és az ebből adódó következményekért a DATA BECKER cég sem szavatosságot, sem jogi felelősséget nem vállal. Az esetlegesen előforduló hibák közlését a szerzők hálásan fogadják.

Gács Péter - Lovász László - Algoritmusok
Ehhez a könyvhöz nincs fülszöveg, de ettől függetlenül még rukkolható/happolható.

Borgulya István - Evolúciós ​algoritmusok
Ehhez a könyvhöz nincs fülszöveg, de ettől függetlenül még rukkolható/happolható.

Ivanyos Gábor - Szabó Réka - Rónyai Lajos - Algoritmusok
Az ​algoritmusokkal és adatszerkezetekkel kapcsolatos első ismeretek mára az informatika alapjainak nélkülözhetetlen részeivé váltak. Ilyen ismeretekre, készségekre mindenkinek szüksége van, aki komolyan foglalkozik programozással és programok tervezésével. Ennek megfelelően kialakult egy eléggé letisztult törzsanyag, amit világszerte oktatnak a számítástechnikai, informatikai képzést nyújtó egyetemi szakokon. Elsődleges célja ennek az anyagnak a feldolgozása. A fontosabb témák a következők: rendezés, keresés, információtömörítés, gráfalgoritmusok, a kiszámíthatóság alapfogalmai, nevezetes bonyolultsági osztályok (P, NP) és algoritmus-tervezési módszerek. A bemutatott algoritmusok tárgyalását példák és feladatok teszik teljessé. A könyv szerzői évek óta tanítanak algoritmikus témájú egyetemi tárgyakat a Budapesti Műszaki Egyetemen és az Eötvös Lorád Tudomány Egyetemen.

Benkő Tiborné - Benkő László - Gyenes Károly - Komócsin Zoltán - Objektum-orientált ​programozás Turbo Pascal nyelven 7.0
A ​Turbo Pascal nyelv (5.0, 5.5, 6.0, 7.0) elsajátításához nyújt a könyv és lemezmelléklete segítséget. A könyv először lépésről-lépésre ismertet meg a Turbo Pascal nyelv alapjaival. A magyarázatokat ábrák és példák teszik szemléletessé. A további fejezetek a moduláris programépítés, a fájlkezelés és a memóriahasználat fogásait, a szöveges és a grafikus képernyő kezelését, a Turbo Pascal 7.0 szabványos moduljait mutatják be. Újdonság az objektum-orientált programozás titkait feltáró fejezet. Tankönyvként - közép- és felsőoktatási intézmények már használják...

Thomas H. Cormen - Charles E. Leiserson - Ronald L. Rivest - Algoritmusok
A ​Műszaki Könyvkiadó Algoritmusok című könyve átfogóan és közérthetően ismerteti az alapvető algoritmusokat és azok elemzését. A hagyományos témakörök mellett olyan modernekkel is foglalkozik, mint az amortizációs elemzés és a párhuzamos algoritmusok. A matematikai elemzések igényesek és részletesek, mégis érthetőek az olvasók széles köre számára. A fejezetek felépítése fokozatos: az egyszerűbb ismeretekkel kezdődnek és a nehezebb anyagrészekkel fejeződnek be. Minden fejezet önálló, egyéni tanulásra is alkalmas. Az algoritmusok szöveges ismertetése mellett a FORTRAN, C vagy Pascal nyelvek ismerői számára könnyen érthető pszeudokódjuk is megtalálható. Nagyszámú példa, ábra, gyakorlat és viszonylag nehezebb feladat is segíti a témakör matematikai és technikai részleteinek alapos megismerését. A szerzők az informatika oktatásának és kutatásának egyik legnagyobb centrumában, a Massachusetts Institute of Technology Elektromérnöki és Számítástudományi Karán, illetve Számítástudományi Laboratóriumában dolgoznak.

Donald E. Knuth - A ​számítógép-programozás művészete 1.
Ehhez a könyvhöz nincs fülszöveg, de ettől függetlenül még rukkolható/happolható.

Rozgonyi-Borus Ferenc - RAM-ba ​zárt világ
Év ​informatika tankönyve `95 Az informatika tankönyvcsaládhoz kapcsolódó RAM-ba zárt világ című számítástechnikai segédkönyv középiskolás szinten, illetve az egyéni tanulásra is alkalmas módon foglalja össze azokat a számítástechnikai ismereteket, amelyek szoftververzióktól függetlenül nélkülözhetetlenek különböző alkalmazások használatához.

Bajalinov Erik - Imreh Balázs - Operációkutatás
Ehhez a könyvhöz nincs fülszöveg, de ettől függetlenül még rukkolható/happolható.

Dusza Árpád - Algoritmusok ​Pascal nyelven
Dusza ​Árpád előző, "Turbo Pascal 6.0 az alapoktól" című könyvében a programnyelv lehetőségeit mutatta be példaprogramok segítségével. Az újabb könyvében az elemi algoritmusokon, az egyszerű adatszerkezeteken van a hangsúly. Ez az, amit tudni kell az érettségin. A könyvben található érettségi feladatok megoldásaiból azt látjuk, hogy az adatszerkezet jó megválasztásával, az elemi algoritmusok és néhány Pascal utasítás ismeretében már meg lehet oldani egy érettségi feladatot. Rövid programokkal, magyarázatokkal tisztázza a könyv a fogalmakat, mutat be programozási fogásokat, alkalmazásokat. Más programozási nyelvet tanulók, tanítók számára is hasznos lehet a könyv. A könyv használatához a http://progtan.hu címen kaphatunk segítséget, ahol feladatokat, algoritmusokat, programokat is találhatunk. Az érettségi feladatok megoldását Pascal, valamint C és Visual Basic nyelven is közzé kívánja tenni a szerző.

Donald E. Knuth - A ​számítógép-programozás művészete - MMIX RISC számítógép az új évezrednek
Ez ​a füzet A számítógép-programozás művészete című monográfia Alapvető algoritmusok című első kötete harmadik kiadását frissíti, és majd a kötet negyedik kiadásának lesz a része. Ez a rész bemutatja a programozóknak a régóta várt MMIX-et, amely egy RISC-alapú számítógép, és a korábban használt MIX gépet váltja fel, továbbá ismerteti az MMIX assembly nyelvet. A könyv új anyagot tartalmaz a szubrutinokról, a korutinokról és az interpretív rutinokról is.

David Sumpter - Outnumbered
Our ​increasing reliance on technology and the internet has opened a window for mathematicians and data researchers to gaze through into our lives. Using the data they are constantly collecting about where we travel, where we shop, what we buy, and what interests us, they can begin to predict our daily habits, and increasingly we are relinquishing our decision-making to algorithms. Are we giving this up too easily? Without understanding what mathematics can and can't do it is impossible to get a handle on how it is changing our lives. Outnumbered is a journey to the dark side of mathematics, from how it dictates our social media activities to our travel routes. David Sumpter investigates whether mathematics is crossing dangerous lines when it comes to what we can make decisions about. This book will show how math impacts all parts of our lives: from the algorithms that decide whom we interact with to the statistical methods that categorize us as potential criminals. It tests financial algorithms that purport to generate money from nothing, and reveals that we are constantly manipulated by the math used by others, from algorithms choosing the news we hear to automated hospital waiting lists deciding whether we receive treatment. Using interviews with those people working at the cutting edge of mathematical and data research, Outnumbered will explain how math and stats work in the real world, and what we should and shouldn't worry about.

Ed Finn - What ​Algorithms Want
We ​depend on—we believe in—algorithms to help us get a ride, choose which book to buy, execute a mathematical proof. It's as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations—the marriage vow, the shaman's curse—do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking. In this book, Ed Finn considers how the algorithm—in practical terms, “a method for solving a problem”—has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking. Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. Drawing on sources that range from Neal Stephenson's Snow Crash to Diderot's Encyclopédie, from Adam Smith to the Star Trek computer, Finn explores the gap between theoretical ideas and pragmatic instructions. He examines the development of intelligent assistants like Siri, the rise of algorithmic aesthetics at Netflix, Ian Bogost's satiric Facebook game Cow Clicker, and the revolutionary economics of Bitcoin. He describes Google's goal of anticipating our questions, Uber's cartoon maps and black box accounting, and what Facebook tells us about programmable value, among other things. If we want to understand the gap between abstraction and messy reality, Finn argues, we need to build a model of “algorithmic reading” and scholarship that attends to process, spearheading a new experimental humanities.

Eric S. Siegel - Predictive ​Analytics
"The ​Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com; former lead analyst at Capital One This book is easily understood by all readers. Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques. You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales. How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt. In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: -What type of mortgage risk Chase Bank predicted before the recession. -Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. -Why early retirement decreases life expectancy and vegetarians miss fewer flights. -Five reasons why organizations predict death, including one health insurance company. -How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. -How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! -How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. -How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. -What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance? Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

Donald E. Knuth - Selected ​Papers on Fun and Games
Donald ​Knuth's influence in computer science ranges from the invention of methods for translating and defining programming languages to the creation of the TEX and METAFONT systems for desktop publishing. His award-winning textbooks have become classics that are often given credit for shaping the field; his scientific papers are widely referenced and stand as milestones of development over a wide variety of topics. The present volume, which is the eighth and final book in his series of collected papers, is the one that he has saved up for dessert: It's a potpourri devoted to recreational aspects of mathematics and computer science, filled with the works that gave him most pleasure during his 50-year career. Here you'll find puzzles, paradoxes, and appealing patterns: visual, numerical, and musical. Nearly fifty of Knuth's works are collected in this book, beginning with his famous first paper in MAD Magazine, and containing several similarly delightful spoofs written "in a jugular vein." Knuth's well-known introduction to the "dancing links" algorithm for combinatorial searches is accompanied by several chapters that shed new light on the age-old problem of knight's tours on a chessboard. There are chapters about word games, computer games, and even basketball, together with topics of modern folk culture such as traffic signs and license plates. Seventeen of these chapters are being published for the first time; fourteen others have appeared only in publications of limited circulation that are difficult to find in libraries. All are found here, together with more than 700 newly created illustrations. CSLI Lecture Notes number 192 Donald E. Knuth is the Fletcher Jones Professor of Computer Science emeritus at Stanford University.

Christopher Steiner - Automate ​This
The ​rousing story of the last gasp of human agency and how today’s best and brightest minds are endeavoring to put an end to it. It used to be that to diagnose an illness, interpret legal documents, analyze foreign policy, or write a newspaper article you needed a human being with specific skills - and maybe an advanced degree or two. These days, high-level tasks are increasingly being handled by algorithms that can do precise work not only with speed but also with nuance. These “bots” started with human programming and logic, but now their reach extends beyond what their creators ever expected. In this fascinating, frightening book, Christopher Steiner tells the story of how algorithms took over - and shows why the “bot revolution” is about to spill into every aspect of our lives, often silently, without our knowledge. The May 2010 “Flash Crash” exposed Wall Street’s reliance on trading bots to the tune of a 998-point market drop and $1 trillion in vanished market value. But that was just the beginning. In Automate This, we meet bots that are driving cars, penning haiku, and writing music mistaken for Bach’s. They listen in on our customer service calls and figure out what Iran would do in the event of a nuclear standoff. There are algorithms that can pick out the most cohesive crew of astronauts for a space mission or identify the next Jeremy Lin. Some can even ingest statistics from baseball games and spit out pitch-perfect sports journalism indistinguishable from that produced by humans. The interaction of man and machine can make our lives easier. But what will the world look like when algorithms control our hospitals, our roads, our culture, and our national security? What hap­pens to businesses when we automate judgment and eliminate human instinct? And what role will be left for doctors, lawyers, writers, truck drivers, and many others? Who knows - maybe there’s a bot learning to do your job this minute.

Peter J. Denning - Matti Tedre - Computational ​Thinking
An ​introduction to computational thinking that traces a genealogy beginning centuries before the digital computer. A few decades into the digital era, scientists discovered that thinking in terms of computation made possible an entirely new way of organizing scientific investigation; eventually, every field had a computational branch: computational physics, computational biology, computational sociology. More recently, "computational thinking" has become part of the K-12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview, tracing a genealogy that begins centuries before digital computers and portraying computational thinking as pioneers of computing have described it. The authors explain that computational thinking (CT) is not a set of concepts for programming; it is a way of thinking that is honed through practice: the mental skills for designing computations to do jobs for us, and for explaining and interpreting the world as a complex of information processes. Mathematically trained experts (known as "computers") who performed complex calculations as teams engaged in CT long before electronic computers. The authors identify six dimensions of today's highly developed CT--methods, machines, computing education, software engineering, computational science, and design--and cover each in a chapter. Along the way, they debunk inflated claims for CT and computation while making clear the power of CT in all its complexity and multiplicity.

Thierry Poibeau - Machine ​Translation
A ​concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and major players in the industry. The dream of a universal translation device goes back many decades, long before Douglas Adams's fictional Babel fish provided this service in The Hitchhiker's Guide to the Galaxy. Since the advent of computers, research has focused on the design of digital machine translation tools--computer programs capable of automatically translating a text from a source language to a target language. This has become one of the most fundamental tasks of artificial intelligence. This volume in the MIT Press Essential Knowledge series offers a concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and market potential. The main approaches are presented from a largely historical perspective and in an intuitive manner, allowing the reader to understand the main principles without knowing the mathematical details. The book begins by discussing problems that must be solved during the development of a machine translation system and offering a brief overview of the evolution of the field. It then takes up the history of machine translation in more detail, describing its pre-digital beginnings, rule-based approaches, the 1966 ALPAC (Automatic Language Processing Advisory Committee) report and its consequences, the advent of parallel corpora, the example-based paradigm, the statistical paradigm, the segment-based approach, the introduction of more linguistic knowledge into the systems, and the latest approaches based on deep learning. Finally, it considers evaluation challenges and the commercial status of the field, including activities by such major players as Google and Systran.

Kartik Hosanagar - A ​Human's Guide to Machine Intelligence
Through ​the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence.

Ed Finn - La ​búsqueda del algoritmo
Finn ​explica de manera pormenorizada cómo internet ha cambiado nuestra cultura y ha abierto nuevas posibilidades creativas: el uso de Google, la confianza en las recomendaciones supuestamente arbitrarias de Spotify o la manera en que compañías como Netflix se sirven de la gestión masiva de datos para dar con productos televisivos de éxito son solo una pequeña parte de un profundo cambio de paradigma que no ha hecho más que empezar. ¿Qué buscan los algoritmos? Sus primeros efectos ya son notorios: el desarrollo de una nueva humanidad, abierta a un proceso de experimentación que se sostiene en el análisis creativo de la información. Ahora bien, ¿queremos realmente lo que los algoritmos quieren para nosotros?

Donald E. Knuth - The ​Art of Computer Programming - Fundamental Algorithms
The ​bible of programming theory and practice is being updated for the first time in more than 20 years. The book is concerned with information structures--the representation of information within a computer, the structural interrelations between data elements and how to work with them efficiently, and applications to simulation, numerical methods and software design.

Robert Sedgewick - Algorithms ​in C
This ​new version of the best-selling book, Algorithms, Second Edition, provides a comprehensive collection of algorithms implemented in C. A variety of algorithms are described in each of the following areas: sorting, searching, string-processing, geometric, graph, and mathematical algorithms. These algorithms are expressed in terms of concise implementations in C, so that readers can both appreciate their fundamental properties and test them on real applications. The treatment of analysis of algorithms is carefully developed. When appropriate, analytic results are discussed to illustratewhy certain algorithms are preferred, and in some cases, the relationship of the practical algorithms being discussed to purely theoretical results is also described. It features hundreds of detailed, innovative figures clearly demonstrate how important algorithms work. Throughout the book, properties sections encapsulate specific information on the performance characteristics of algorithms. Six chapters present fundamental concepts, including a brief introduction to data structures. Algorithms in C provides readers with the tools to confidently implement, run, and debug useful algorithms.

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Ismeretlen szerző - Informatikai ​algoritmusok 1-2.
Az ​Informatikai algoritmusok két kötetből áll. Minkét kötet hat részre tagolódik. Az első kötet 17 fejezetet tartalmaz. A könyvben ismertetett algoritmusok megértését 247 ábra, 157 pszeudokód és 133 példa, az önálló tanulást 269 gyakorlat és 66 feladat segíti. Az irodalomjegyzék több mint 500 hivatkozást tartalmaz, amelyek - a külföldi szakirodalom mellett - igyekeznek az adott témakörök teljes magyar nyelvű szakirodalmát is tükrözni.

Virginia Eubanks - Automating ​Inequality
A ​powerful investigative look at data-based discrimination—and how technology affects civil and human rights and economic equity The State of Indiana denies one million applications for healthcare, foodstamps and cash benefits in three years—because a new computer system interprets any mistake as “failure to cooperate.” In Los Angeles, an algorithm calculates the comparative vulnerability of tens of thousands of homeless people in order to prioritize them for an inadequate pool of housing resources. In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect. Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor. In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile. The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values. This deeply researched and passionate book could not be more timely.

Donald E. Knuth - A ​számítógép-programozás művészete 2.
Ehhez a könyvhöz nincs fülszöveg, de ettől függetlenül még rukkolható/happolható.

Donald E. Knuth - A ​számítógép-programozás művészete - Permutációk és n-esek előállítása
A ​sorozat ezen része annak ismertetésével kezdődik, hogyan állítható elő minden lehetőség. A szerző elemzi az összes n-es előállítását, majd a módszereket kiterjeszti minden permutáció generálására. Ezek az algoritmusok természetes alapot szolgáltatnak a kombinatorikus matematika kulcsgondolatainak bevezetéséhez és kutatásához. Ebben és a többi részben Knuth fontos elméleteket magyaráz meg a kapcsolódó játékok és rejtvények elemzésével.

Thomas H. Cormen - Charles E. Leiserson - Ronald L. Rivest - Clifford Stein - Introduction ​to Algorithms
There ​are books on algorithms that are rigorous but incomplete and others that cover masses of material but lack rigor. Introduction to Algorithms combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

Donald E. Knuth - The ​Art of Computer Programming - Seminumerical Algorithms
The ​second volume offers a complete introduction to the field of seminumerical algorithms, with separate chapters on random numbers and arithmetic. The book summarizes the major paradigms and basic theory of such algorithms, thereby providing a comprehensive interface between computer programming and numerical analysis. Particularly noteworthy in this third edition is Knuth's new treatment of random number generators, and his discussion of calculations with formal power series.

Ismeretlen szerző - Tudomány ​és technika & Commodore 64
Ehhez a könyvhöz nincs fülszöveg, de ettől függetlenül még rukkolható/happolható.

John D. Kelleher - Deep ​Learning
An ​accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning--major trends, possible developments, and significant challenges.

Michael Kearns - Aaron Roth - The ​Ethical Algorithm
Over ​the course of a generation, algorithms have gone from mathematical abstractions to powerful mediators of daily life. Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps. Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.

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