From Theory to Algorithms

Author: Shai Shalev-Shwartz,Shai Ben-David

Publisher: Cambridge University Press

ISBN: 1107057132

Category: Computers

Page: 409

View: 9266

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
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Ein verständlicher Einstieg mit Python

Author: Tariq Rashid

Publisher: O'Reilly

ISBN: 3960101031

Category: Computers

Page: 232

View: 9451

Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Sie sind Grundlage vieler Anwendungen im Alltag wie beispielsweise Spracherkennung, Gesichtserkennung auf Fotos oder die Umwandlung von Sprache in Text. Dennoch verstehen nur wenige, wie neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie neuronale Netze arbeiten: - Zunächst lernen Sie die mathematischen Konzepte kennen, die den neuronalen Netzen zugrunde liegen. Dafür brauchen Sie keine tieferen Mathematikkenntnisse, denn alle mathematischen Ideen werden behutsam und mit vielen Illustrationen und Beispielen erläutert. Eine Kurzeinführung in die Analysis unterstützt Sie dabei. - Dann geht es in die Praxis: Nach einer Einführung in die populäre und leicht zu lernende Programmiersprache Python bauen Sie allmählich Ihr eigenes neuronales Netz mit Python auf. Sie bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. - Im nächsten Schritt tunen Sie die Leistung Ihres neuronalen Netzes so weit, dass es eine Zahlenerkennung von 98 % erreicht – nur mit einfachen Ideen und simplem Code. Sie testen das Netz mit Ihrer eigenen Handschrift und werfen noch einen Blick in das mysteriöse Innere eines neuronalen Netzes. - Zum Schluss lassen Sie das neuronale Netz auf einem Raspberry Pi Zero laufen. Tariq Rashid erklärt diese schwierige Materie außergewöhnlich klar und verständlich, dadurch werden neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.
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Author: Ethem Alpaydın

Publisher: Oldenbourg Verlag

ISBN: 9783486581140

Category:

Page: 440

View: 7019

Unter maschinellem Lernen versteht man die kunstliche Generierung von Wissen aus Erfahrung. Das vorliegende Buch diskutiert Methoden aus den Bereichen Statistik, Mustererkennung etc. und versucht, die unterschiedlichen Ansatze zu kombinieren, um moglichst effiziente Losungen zu finden."
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The Ultimate Beginner's Guide to Understanding Machine Learning

Author: Sebastian Dark

Publisher: N.A

ISBN: 9781723821257

Category: Computers

Page: 82

View: 805

Curious to learn about the revolutionary technology that is shaping our future and changing the world? Machine learning is a part of the field of computer science that involves computer systems being able to "learn" with data despite not being programmed explicitly. In 2017, AlphaGo, which is AI developed by Google DeepMind and started off by only knowing the rules of the game, was eventually able to train itself and beat Ke Jie, the world No.1 ranked player at the time. Although this may not seem that impressive at first, it is important to understand that Go is a very complex game that many programmers were not able to trump with AI in the past. Although Go is an interesting example, the possibilities of using machine learning are limitless. From retail to medicine to finance, machine learning has the ability to change each industry it comes into contact with. In fact, this revolution has already begun and will only continue to get bigger. Without a doubt it is the future. However, it is as complex as it is revolutionary. If you do not have a background or any experience in the field, it is easy to get bogged down by all the complicated concepts and term. Furthermore, finding information that is easy to understand can prove to be a challenge because it most likely will not be thorough even if you do find it. Some of the things you will learn include... What Machine Learning Really is and How It Can Change the World The Fields of Study and Subjects Involved Various Applications of Machine Learning Supervised vs Unsupervised Learning Neural Networks Deep Learning And much more! In this book, you will find the perfect balance between the information being very thorough and being able to understand it. Although tailored for beginners, it won't contain simple and easily accessible information. You will dive deep into machine learning but you will be carefully led through it in a way that will make everything easy to understand even if you do not have a technical background in computer programming. Whether you are looking to gain knowledge for a potential career in machine learning, want to learn how this will impact our future, or simply want to satisfy your curiosity about potentially the greatest technological advancement of our time, this book will help tremendously in understanding machine learning. If you are finally prepared to understand this revolutionary yet complex technology at a high level despite what your technical background may be, Purchase Now! **Get the Kindle eBook version for FREE when you buy the Paperback version of this book!**
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Author: Allen B. Downey

Publisher: O'Reilly Germany

ISBN: 3868993436

Category: Computers

Page: 160

View: 9856

Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.
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Author: Matthew Scarpino

Publisher: John Wiley & Sons

ISBN: 3527818960

Category: Computers

Page: 324

View: 3579

TensorFlow ist Googles herausragendes Werkzeug für das maschinelle Lernen, und dieses Buch macht es zugänglich, selbst wenn Sie bisher wenig über neuronale Netze und Deep Learning wissen. Sie erfahren, auf welchen Prinzipien TensorFlow basiert und wie Sie mit TensorFlow Anwendungen schreiben. Gleichzeitig lernen Sie die Konzepte des maschinellen Lernens kennen. Wenn Sie Softwareentwickler sind und TensorFlow in Zukunft einsetzen möchten, dann ist dieses Buch der richtige Einstieg für Sie. Greifen Sie auch zu, wenn Sie einfach mehr über das maschinelle Lernen erfahren wollen.
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an Absolute Beginner’s Guide to Learning and Understanding Machine Learning Successfully

Author: Ryan Hill

Publisher: Karen Gonzales via PublishDrive

ISBN: N.A

Category: Computers

Page: 183

View: 726

★★★ MACHINE LEARNING FOR ABSOLUTE BEGINNERS ★★★ Do you want to know about Machine Learning even as a beginner? You have come to the right place Machine learning is one of the hottest topics in this century - for good reasons. A neural network is often mentioned but covers only a small part of machine learning. There is much more to explore. There are a lot of interested people out there but many do not know where to start. The difficult question basically is how to start actually learning it? Especially beginners might get discouraged because of statistics and math which is an integral part of machine learning. None the less you do not need to be a math expert to apply machine learning. This machine learning course is here to show you why. Instead of telling you all the statistics and math behind the Algorithms, I prefer to give you a much more hands on approach. At the end of the day there's only one thing that really counts - THE RESULT. What you will learn Introduction to Machine Learning What is Machine Learning… And why should we care? The 6 Steps of Machine Learning What neural networks have to do with machine learning What neural networks have to do with deep learning? What machine learning algorithms can do The different machine learning applications and their disadvantages and advantages What machine learning have in store for us? How Machine Learning is Fighting Cancer Who is the target audience? Beginners in machine learning People who like a hands-on approach and not only watching People who prefer practice instead of theory All people who want to dive into one of the hottest topics out there but do not know where to start You want to take advantage of the data driven opportunity ahead ★★★ Don’t wait any longer! Scroll up and click the BUY NOW button to begin the journey of learning machine learning even as an absolute ML beginner! ★★★
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First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings

Author: Danail Stoyanov,Zeike Taylor,Seyed Mostafa Kia,Ipek Oguz,Mauricio Reyes,Anne Martel,Lena Maier-Hein,Andre F. Marquand,Edouard Duchesnay,Tommy Löfstedt,Bennett Landman,M. Jorge Cardoso,Carlos A. Silva,Sergio Pereira,Raphael Meier

Publisher: Springer

ISBN: 3030026280

Category: Computers

Page: 149

View: 3888

This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.
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The Ultimate Beginners Guide to Understanding Machine Learning Basics & Techniques

Author: Richard Dumont

Publisher: Createspace Independent Publishing Platform

ISBN: 9781976579554

Category:

Page: 76

View: 5118

Get an In-Depth Understanding of Machine Learning In Only 60 Minutes. Imagine that you just need a 60 min read to learn about all the basic techniques involved in Machine Learning. No extremely hard formulated grammatical texts, just plain and simple English. What if you had a guide that would take you through the essential elements of Machine Learning? With over a decade of experience programming expert, Richard Dumont decided to share his knowledge with his audience. He created the perfect outline for a complete newbie to Machine Learning. If you are new to the world of computer science than this is for you! In this book, you'll learn: -How IoT can advantage from Machine Learning -How can you find the best machine learning for you? -What the Difference is between Machine Learning Techniques -How Machine Learning personalization adds to your bottom line -How Machine Learning Influences Your Productivity -How Machine Learning Is Improving Companies Work Processes And lots more... Buy this book NOW and Get an In-Depth Understanding of Machine Learning In Only 60 Minutes. Pick up your copy right now by clicking the BUY NOW button at the top of this page!
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A Case-Based Approach to Understanding Deep Neural Networks

Author: Umberto Michelucci

Publisher: Apress

ISBN: 1484237900

Category: Computers

Page: 410

View: 7633

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). What You Will Learn Implement advanced techniques in the right way in Python and TensorFlow Debug and optimize advanced methods (such as dropout and regularization) Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) Set up a machine learning project focused on deep learning on a complex dataset Who This Book Is For Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.
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With Time Series and Industry-Based Use Cases in R

Author: Karthik Ramasubramanian,Abhishek Singh

Publisher: Apress

ISBN: 1484242157

Category: Computers

Page: 700

View: 9759

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R. As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning. What You'll Learn Understand machine learning algorithms using R Master the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithms See industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R Who This Book is For Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.
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Das Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning

Author: Sebastian Raschka

Publisher: MITP-Verlags GmbH & Co. KG

ISBN: 3958454240

Category: Computers

Page: 424

View: 4753

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First International Workshop, MLDM'99, Leipzig, Germany, September 16-18, 1999, Proceedings

Author: Petra Perner,Maria Petrou

Publisher: Springer Science & Business Media

ISBN: 3540665994

Category: Computers

Page: 224

View: 590

The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject.
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Case Studies and Algorithms to Get You Started

Author: Drew Conway,John Myles White

Publisher: "O'Reilly Media, Inc."

ISBN: 1449330533

Category: Computers

Page: 324

View: 7204

If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data
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Author: Seth Raymond

Publisher: Independently Published

ISBN: 9781720253525

Category: Computers

Page: 436

View: 5031

Over the coming decades, Artificial Intelligence will profoundly impact the way we live, work, wage war, play, seek a mate, educate our young, and care for our elderly. It is likely to greatly increase our aggregate wealth, but it will also upend our labor markets, reshuffle our social order, and strain our private and public institutions. Eventually it may alter how we see our place in the universe, as machines pursue goals independent of their creators and outperform us in domains previously believed to be the sole dominion of humans. Whether we regard them as conscious or unwitting, revere them as a new form of life or dismiss them as mere clever appliances, is beside the point. They are likely to play an increasingly critical and intimate role in many aspects of our lives. Inside, you'll get a clear overview of the technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field.
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Verstehe deine Vergangenheit, ordne deine Gegenwart, gestalte deine Zukunft

Author: Ryder Carroll

Publisher: Rowohlt Verlag GmbH

ISBN: 3644403228

Category: Self-Help

Page: 352

View: 755

Der Erfinder der bahnbrechenden Bullet-Journal-Methode Ryder Carroll zeigt in diesem Buch, wie Sie endlich zum Pilot Ihres Lebens werden und nicht länger Passagier bleiben. Seine Methode hilft mit einer strukturierteren Lebensweise achtsamer und konzentrierter zu werden. Inzwischen lassen sich Millionen Menschen von ihm inspirieren. In diesem Buch erklärt er seine Philosophie und zeigt, wie Sie Klarheit ins Gedankenchaos bringen, wie Sie Ihre täglichen Routinen entwickeln und vage Vorhaben in erreichbare Ziele verwandeln. Mit nur einem Stift und einem Notizblock und Carrolls revolutionärer Technik werden Sie produktiver, fokussierter und lernen, was wirklich zählt - bei der Arbeit und im Privaten.
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Thinking with Examples for Effective Learning

Author: Shan Suthaharan

Publisher: Springer

ISBN: 1489976418

Category: Business & Economics

Page: 359

View: 590

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
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Die Singularität naht

Author: Ray Kurzweil

Publisher: Lola Books

ISBN: 3944203135

Category: Technology & Engineering

Page: 672

View: 5108

Das Jahr 2045 markiert einen historischen Meilenstein: Es ist das Jahr, in dem der Mensch seine biologischen Begrenzungen mithilfe der Technik überwinden wird. Diese als technologische Singularität bekannt gewordene Revolution wird die Menschheit für immer verändern. Googles Chefingenieur Ray Kurzweil, dessen wahnwitzigen Visionen in den vergangenen Jahrzehnten immer wieder genau ins Schwarze trafen, zeichnet in diesem Klassiker des Transhumanismus mit beispielloser Detailwut eine bunt schillernde Momentaufnahme der technischen Evolution und legt dar, weshalb diese so bald kein Ende finden, sondern im Gegenteil immer weiter an Dynamik gewinnen wird. Daraus ergibt sich eine ebenso faszinierende wie schockierende Vision für die Zukunft der Menschheit.
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Author: Joseph Howse

Publisher: Packt Publishing Ltd

ISBN: 1783287381

Category: Computers

Page: 302

View: 7584

This book is for programmers who want to expand their skills by building fun, smart, and useful systems with OpenCV. The projects are ideal in helping you to think creatively about the uses of computer vision, natural user interfaces, and ubiquitous computers (in your home, car, and hand).
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