Neural Networks for Complete Beginners

Introduction for Neural Network Programming
Author: Mark Smart
Publisher: Createspace Independent Publishing Platform
ISBN: 9781543268720
Category:
Page: 94
View: 6928

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This book is an exploration of an artificial neural network. It has been created to suit even the complete beginners to artificial neural networks. The first part of the book is an overview of artificial neural networks so as to help the reader understand what they are. You will also learn the relationship between the neurons which make up the human brain and the artificial neurons. Artificial neural networks embrace the concept of learning which is common in human beings. This book guides you to understand how learning takes place in artificial neural networks. The back-propagation algorithm, which is used for training artificial neural networks, is discussed. The book also guides you through the architecture of an artificial neural network. The various types of artificial neural networks based on their architecture are also discussed. The book guides you on the necessary steps for one to build a neural network. The perception, which is a type of an artificial neural network, is explored, and you will explore how to implement one programmatically. The following topics are discussed in this book: -What is a Neural Network? -Learning in Neural Networks -The Architecture of Neural Networks -Building Neural Networks -The Perceptron

Neuronale Netze selbst programmieren

Ein verständlicher Einstieg mit Python
Author: Tariq Rashid
Publisher: O'Reilly
ISBN: 3960101031
Category: Computers
Page: 232
View: 3395

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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.

Artificial Neural Networks

Concepts, Tools and Techniques Explained for Absolute Beginners
Author: François Duval
Publisher: Createspace Independent Publishing Platform
ISBN: 9781985134560
Category:
Page: 128
View: 4687

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***** Buy now (Will soon return to $75.99 + Special Offer Below) ***** Free Kindle eBook for customers who purchase the print book from Amazon Are you thinking of learning more about Artificial Neural Network? This book has been written in layman's terms as an introduction to neural networks and their algorithms. Each algorithm is explained very easily for more understanding. Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images which detail all algorithms and their applications in all area of the real life. Why this book is different ? An Artificial Neural Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human (animal) brain processes information. It includes a large number of connected processing units called neurons that work together to process information. They also generate meaningful results from it. In this book, we will take you through the complete introduction to Artificial Neural Network, Artificial Neural Network Structure, layers of ANN, Applications, Algorithms, Tools and technology, Practical implementations and the benefits and limitations of ANN. This book takes a different approach that is based on providing simple examples of how ANN algorithms work, and building on those examples step by step to encompass the more complicated parts of the algorithms. Target Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach ANN, but are too afraid of complex math to start Newbies in computer science techniques and ANN Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on neural networks and deep learning What's inside this book? What is Artificial Neural Network? Why Neural Networks? Major Variants of Artificial Neural Network Tools and Technologies Practical implementations Major NN projects Open sources resources Issues and Challenges Applications of ANN Deep Learning: What & Why? Our Future with Deep Learning Applied The Long-Term Vision of Deep Learning Glossary of Some Useful Terms in Neural Networks Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to learn more about deep learning with practical applications, this book is for you. This book has been written in layman's terms as an introduction to neural networks and their algorithms. Each algorithm is explained very easily for more understanding. No coding experience is required. Some practical examples is presented with Python but it is not the major part of the book. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a Neural Networks expert? A: Unfortunately, no. This book is designed for readers taking their first steps in neural networks and further learning will be required beyond this book to master all aspects of neural networks. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. will also be happy to help you if you send us an email at [email protected]

Artificial Neural Networks

An Introduction
Author: Kevin L. Priddy,Paul E. Keller
Publisher: SPIE Press
ISBN: 9780819459879
Category: Technology & Engineering
Page: 165
View: 2470

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This tutorial text provides the reader with an understanding of artificial neural networks (ANNs) and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.

Machine Learning for Absolute Beginners

A Plain English Introduction
Author: Oliver Theobald
Publisher: Independently Published
ISBN: 9781549617218
Category: Computer algorithms
Page: 160
View: 2855

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"The manner in which computers are now able to mimic human thinking to process information is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the modern age of machine learning, computers do not strictly need to receive an 'input command' to perform a task, but rather 'input data'. From the input of data they are able to form their own decisions and take actions virtually as a human world. But given it is a machine, it can consider many more scenarios and execute far more complicated calculations to solve complex problems. This is the element that excites data scientists and machine learning engineers the most. The ability to solve complex problems never before attempted. This book will dive in to introduce machine learning, and is ideal for beginners starting out in machine learning."--page 4 of cover.

Theorie der neuronalen Netze

Eine systematische Einführung
Author: Raul Rojas
Publisher: Springer-Verlag
ISBN: 3642612318
Category: Computers
Page: 446
View: 6396

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Neuronale Netze sind ein Berechenbarkeitsparadigma, das in der Informatik zunehmende Beachtung findet. In diesem Buch werden theoretische Ansätze und Modelle, die in der Literatur verstreut sind, zu einer modellübergreifenden Theorie der künstlichen neuronalen Netze zusammengefügt. Mit ständigem Blick auf die Biologie wird - ausgehend von einfachsten Netzen - gezeigt, wie sich die Eigenschaften der Modelle verändern, wenn allgemeinere Berechnungselemente und Netztopologien eingeführt werden. Jedes Kapitel enthält Beispiele und ist ausführlich illustriert und durch bibliographische Anmerkungen abgerundet. Das Buch richtet sich an Leser, die sich einen Überblick verschaffen oder vorhandene Kenntnisse vertiefen wollen. Es ist als Grundlage für Neuroinformatikvorlesungen an deutschsprachigen Universitäten geeignet.

Python Artificial Intelligence Projects for Beginners

Get up and running with Artificial Intelligence using 8 smart and exciting AI applications
Author: Joshua Eckroth
Publisher: Packt Publishing Ltd
ISBN: 1789538246
Category: Computers
Page: 162
View: 5064

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Build smart applications by implementing real-world artificial intelligence projects Key Features Explore a variety of AI projects with Python Get well-versed with different types of neural networks and popular deep learning algorithms Leverage popular Python deep learning libraries for your AI projects Book Description Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence. This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library. By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress What you will learn Build a prediction model using decision trees and random forest Use neural networks, decision trees, and random forests for classification Detect YouTube comment spam with a bag-of-words and random forests Identify handwritten mathematical symbols with convolutional neural networks Revise the bird species identifier to use images Learn to detect positive and negative sentiment in user reviews Who this book is for Python Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with code

Hands-On Artificial Intelligence for Beginners

An introduction to AI concepts, algorithms, and their implementation
Author: Patrick D. Smith
Publisher: Packt Publishing Ltd
ISBN: 1788992261
Category: Computers
Page: 362
View: 8108

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Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with ease Key Features Enter the world of AI with the help of solid concepts and real-world use cases Explore AI components to build real-world automated intelligence Become well versed with machine learning and deep learning concepts Book Description Virtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world. Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You'll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games. By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications. What you will learn Use TensorFlow packages to create AI systems Build feedforward, convolutional, and recurrent neural networks Implement generative models for text generation Build reinforcement learning algorithms to play games Assemble RNNs, CNNs, and decoders to create an intelligent assistant Utilize RNNs to predict stock market behavior Create and scale training pipelines and deployment architectures for AI systems Who this book is for This book is designed for beginners in AI, aspiring AI developers, as well as machine learning enthusiasts with an interest in leveraging various algorithms to build powerful AI applications.

Machine Learning for Absolute Beginners: an Absolute Beginner’s Guide to Learning and Understanding Machine Learning Successfully


Author: Ryan Hill
Publisher: Ryan Hill
ISBN: 1386263052
Category: Computers
Page: N.A
View: 6424

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★★★ 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! ★★★

Nonlinear Physics for Beginners

Fractals, Chaos, Solitons, Pattern Formation, Cellular Automata and Complex Systems
Author: Lui Lam
Publisher: World Scientific Publishing Company
ISBN: 9813103701
Category: Science
Page: 348
View: 8853

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Almost all real systems are nonlinear. For a nonlinear system the superposition principle breaks down: The system's response is not proportional to the stimulus it receives; the whole is more than the sum of its parts. The three parts of this book contains the basics of nonlinear science, with applications in physics. Part I contains an overview of fractals, chaos, solitons, pattern formation, cellular automata and complex systems. In Part II, 14 reviews and essays by pioneers, as well as 10 research articles are reprinted. Part III collects 17 students projects, with computer algorithms for simulation models included. The book can be used for self-study, as a textbook for a one-semester course, or as supplement to other courses in linear or nonlinear systems. The reader should have some knowledge in introductory college physics. No mathematics beyond calculus and no computer literacy are assumed. Request Inspection Copy

TensorFlow für Dummies


Author: Matthew Scarpino
Publisher: John Wiley & Sons
ISBN: 3527818960
Category: Computers
Page: 324
View: 1481

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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.

Grundlagen zur Neuroinformatik und Neurobiologie

The Computational Brain in deutscher Sprache
Author: Patricia S. Churchland,Terrence J. Sejnowski
Publisher: Springer-Verlag
ISBN: 3322868214
Category: Technology & Engineering
Page: 702
View: 6493

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The Computational Brain, das außergewöhnliche Buch über vergleichende Forschung in den Bereichen von menschlichem Gehirn und neuesten Möglichkeiten der Computertechnologie, liegt hiermit erstmals in deutscher Sprache vor. Geschrieben von einem führenden Forscherteam in den USA, ist es eine Fundgrube für alle, die wissen wollen, was der Stand der Wissenschaft auf diesem Gebiet ist. Die Autoren führen die Bereiche der Neuroinformatik und Neurobiologie mit gut ausgesuchten Beispielen und der gebotenen Hintergrundinformation gekonnt zusammen. Das Buch wird somit nicht nur dem Fachwissenschaftler sondern auch dem interdisziplinären Interesse des Informatikers und des Biologen auf eine hervorragende Weise gerecht. Übersetzt wurde das Buch von Prof. Dr. Steffen Hölldobler und Dipl.-Biol. Claudia Hölldobler, einem Informatiker und einer Biologin. Rezension in Spektrum der Wissenschaft nr. 10, S. 122 f. im Oktober 1997 (...) Die 1992 erschienene amerikanische Originalausgabe des vorliegenden Werkes ist so erfolgreich, daß man bereits von einem Klassiker reden kann. (...) (...) ....ist das Buch sehr zu empfehlen. In Verbindung von Neurobiologie und Neuroinformatik konkurrenzlos, vermittelt es einiges von der Faszination theoretischer Hirnforschung, die auch in Deutschland zunehmend mehr Wissenschaftler in ihren Bann schlägt. Rezension erschienen in: Computer Spektrum 3/1997, S. 2 (...)Das Buch wird somit nicht nur dem Fachwissenschaftler, sondern auch den interdisziplinären Interesse des Informatikers und des Biologen auf eine hervorragende Weise gerecht(...)

Access 2007 für Dummies


Author: Laurie Ulrich Fuller,Ken Cook,John Kaufeld
Publisher: John Wiley & Sons
ISBN: 3527657320
Category: Computers
Page: 425
View: 1758

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Mit Word findet man sich recht schnell zurecht, Excel ist schon komplexer, aber Access ist wirklich nicht leicht zu beherrschen - so die Meinung vieler Office-Nutzer. Mit diesem Buch schon, behaupten wir! Die Autoren erkl?ren selbst Lesern ohne Datenbankvorkenntnisse, wie man mit Access Daten organisiert und ausgibt. Vom ersten Schritt bis zum Datenbankexperten! Dabei gehen die Autoren auf die Unterschiede zwischen der alten und neuen Version ein.

Statistik-Workshop für Programmierer


Author: Allen B. Downey
Publisher: O'Reilly Germany
ISBN: 3868993436
Category: Computers
Page: 160
View: 3834

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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.

Neural Networks and Deep Learning

Neural Networks and Deep Learning, Deep Learning, Big Data
Author: Pat Nakamoto
Publisher: Createspace Independent Publishing Platform
ISBN: 9781722147778
Category:
Page: 148
View: 471

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What's Inside? This includes 3 manuscripts: Book 1: Neural Networks & Deep Learning: Deep Learning explained to your granny - A visual introduction for beginners who want to make their own Deep Learning Neural Network... What you will gain from this book: * A deep understanding of how Deep Learning works * A basics comprehension on how to build a Deep Neural Network from scratch Who this book is for: * Beginners who want to approach the topic, but are too afraid of complex math to start! * Two main Types of Machine Learning Algorithms * A practical example of Unsupervised Learning * What are Neural Networks? * McCulloch-Pitts's Neuron * Types of activation function * Types of network architectures * Learning processes * Advantages and disadvantages * Let us give a memory to our Neural Network * The example of book writing Software * Deep learning: the ability of learning to learn * How does Deep Learning work? * Main architectures and algorithms * Main types of DNN * Available Frameworks and libraries * Convolutional Neural Networks * Tunnel Vision * Convolution * The right Architecture for a Neural Network * Test your Neural Network * A general overview of Deep Learning * What are the limits of Deep Learning? * Deep Learning: the basics * Layers, Learning paradigms, Training, Validation * Main architectures and algorithms * Models for Deep Learning * Probabilistic graphic models * Restricted Boltzmann Machines * Deep Belief Networks Book2: Deep Learning: Deep Learning explained to your granny - A guide for Beginners... What's Inside? * A general overview of Deep Learning * What are the limits of Deep Learning? * Deep Learning: the basics * Layers, Learning paradigms, Training, Validation * Main architectures and algorithms * Convolutional Neural Networks * Models for Deep Learning * Probabilistic graphic models * Restricted Boltzmann Machines * Deep Belief Networks * Available Frameworks and libraries * TensorFlow Book 3: Big Data: The revolution that is transforming our work, market and world... "Within 2 days we produce the same amount of data generated by at the beginning of the civilization until 2003," said Eric Schmidt in 2010. According to IBM, by 2020 the world will have generated a mass of data on the order of 40 zettabyte (1021Byte). Just think, for example, of digital content such as photos, videos, blogs, posts, and everything that revolves around social networks; only Facebook marks 30 billion pieces of content each month shared by its users. The explosion of social networks, combined with the emergence of smartphones, justifies the fact that one of the recurring terms of recent years in the field of innovation, marketing and IT is "Big Data." The term Big Data indicates data produced in massive quantities, with remarkable rapidity and in the most diverse formats, which require technologies and resources that go far beyond conventional data management and storage systems. In order to obtain from the use of this data the maximum results in the shortest possible time or even in real time, specific tools with high computing capabilities are necessary. But what does the Big Data phenomenon mean? Is the proliferation of data simply the sign of an increasingly invasive world? Or is there something more to it? Pat Nakamoto will guide you through the discovery of the world of Big data, which, according to experts, in the near future could become the new gold or oil, in what is a real Data Driven economy.