Einfuhrung In Deep Learning

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Learning deep

Author : Benjamin Säfken
Publisher : Göttingen University Press
ISBN 13 : 3863954629
Total Pages : 160 pages
Book Rating : 4.8/5 (639 download)

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Book Synopsis Learning deep by : Benjamin Säfken

Download or read book Learning deep written by Benjamin Säfken and published by Göttingen University Press. This book was released on 2020 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence is considered to be one of the most decisive topics in the 21th century. Deep learning algorithms, which are the basis of artificial intelligence applications, are of central interest for researchers but also for students that strive to build up academic knowledge and practical competences in this field. The Deep Learning Seminar at the University of Göttingen follows the central notion of the Humboldtian model of higher education and offers graduate students of applied statistics the opportunity to conduct their own research. The quality of the results motivated us to publish the most promising seminar papers in this volume. For the selected papers a full peer review process was conducted. The presented contributions cover a broad range of deep learning topics. The articles in the first part of this volume may serve the reader as introduction to deep learning algorithms. Subsequently, research applications allow the reader to gain deep insights into some of the latest developments in the field of artificial intelligence.

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Learning Methods for Automotive Radar Signal Processing

Author : Rodrigo Pérez González
Publisher : Cuvillier Verlag
ISBN 13 : 3736964625
Total Pages : 136 pages
Book Rating : 4.7/5 (369 download)

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Book Synopsis Deep Learning Methods for Automotive Radar Signal Processing by : Rodrigo Pérez González

Download or read book Deep Learning Methods for Automotive Radar Signal Processing written by Rodrigo Pérez González and published by Cuvillier Verlag. This book was released on 2021-06-28 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Um autonomes Fahren zu ermöglichen, müssen zukünftige Sensorsysteme nicht nur in der Lage sein, das Fahrumfeld zu erfassen, sondern auch semantische Informationen zu liefern. In dieser Arbeit werden Deep Learning Methoden, die die klassische Radarsignalverarbeitungskette verbessern oder sogar ersetzen sollen, entwickelt und im Hinblick auf das Automobilumfeld evaluiert. Für diesen Zweck werden hochmoderne Bilderkennungsalgorithmen auf die Domäne der Radarsignale angepasst und zur Klassifizierung und Detektion verschiedener Verkehrsteilnehmer angewendet. For autonomous driving to become a reality, future sensor systems must be able to not only capture the vehicle’s environment, but also to provide semantic information. In this work, deep learning methods, meant to enhance—or even replace—the classical radar signal processing chain, are developed and evaluated in the context of automotive applications. For this purpose, state of the art computer vision approaches are adapted and applied to radar signals in order to detect and classify different road users.

Responsible Industry 4.0

Author : David Mhlanga
Publisher : Taylor & Francis
ISBN 13 : 1000994406
Total Pages : 283 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Responsible Industry 4.0 by : David Mhlanga

Download or read book Responsible Industry 4.0 written by David Mhlanga and published by Taylor & Francis. This book was released on 2023-11-13 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growth of digital technologies and Artificial Intelligence (AI) has the potential to increase global wealth and tackle some of the world's most serious problems, including economic marginalization. In contrast, these innovations may contribute to widening economic gaps and potentially threaten the stability of democratic governments. Changes in economic growth and distribution are occurring in both developed and developing countries due to the impact that new technologies are having on businesses and the workplace. This book provides answers to the question of whether the technologies of the Fourth Industrial Revolution, such as Artificial Intelligence, will lead to greater economic uncertainty, environmental collapse, and social unrest or whether they will help achieve shared prosperity and sustainable development, and successively the Sustainable Development Goals (SDGs). The book focuses on Human-centered Artificial Intelligence (AI) and the responsible deployment of diverse technologies for achieving sustainable development. It examines why the human-centred approach is so crucial to long-term success. It underlines the definition of human-centred AI and its importance in creating sustainability and resilience. It starts with a comprehensive history of the Fourth Industrial Revolution, details how to produce artificial intelligence that is centred on humans in order to accomplish sustainable development in the era of the Fourth Industrial Revolution, and then goes on to discuss the need for proper management and governance of emerging technologies. The book ends by presenting a framework that will assist stakeholders in employing AI in a human-centred manner to achieve the sustainable development goals set for 2030. The book will appeal to scholars, academics and researchers interested in information linked to the SDGs, AI and industry 4.0.

Machine Learning in Action

Author : Peter Harrington
Publisher : Simon and Schuster
ISBN 13 : 1638352453
Total Pages : 558 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Machine Learning in Action by : Peter Harrington

Download or read book Machine Learning in Action written by Peter Harrington and published by Simon and Schuster. This book was released on 2012-04-03 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

Powertrain Development with Artificial Intelligence

Author : Aras Mirfendreski
Publisher : Springer Nature
ISBN 13 : 3662638630
Total Pages : 176 pages
Book Rating : 4.6/5 (626 download)

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Book Synopsis Powertrain Development with Artificial Intelligence by : Aras Mirfendreski

Download or read book Powertrain Development with Artificial Intelligence written by Aras Mirfendreski and published by Springer Nature. This book was released on 2022-04-04 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: The variety of future powertrain concepts has drastically increased the development cost for automotive manufactures. Profitable investment requires a significantly leaner and efficient powertrain development process. Traditional methods of test and model based development need to be assisted by big data and data analytics. For this purpose, a valuable tool is available at the right time - artificial intelligence (AI). But what does AI really mean in a narrower sense? What concepts lie behind it? And how are the methods and algorithms transferable to powertrain applications? For the first time, this book aims to bridge the gap between automotive engineering and computer science, by illuminating the complexity of current AI concepts and demystifying it for powertrain applications. By elaborating on work processes, it shows how AI could be implemented and how completely novel methods can help us reshape the future of mobility.

AI on The Job

Author : Andreas Moring
Publisher : Springer Nature
ISBN 13 : 3662640058
Total Pages : 140 pages
Book Rating : 4.6/5 (626 download)

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Book Synopsis AI on The Job by : Andreas Moring

Download or read book AI on The Job written by Andreas Moring and published by Springer Nature. This book was released on 2022-01-01 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a practical guide to using artificial intelligence with motivated employees in companies and organizations. You will learn what the prerequisites are for people to look forward to productive collaboration with "intelligent machines". Because this is the only way to leverage the full potential of AI. To this end, you will receive an overview of how and where AI can be used in companies and how to identify the right areas of application for AI in your company. The main issue here is the following: which tasks will be taken over by AI in the future and which should continue to be performed by employees. These decisions change processes and tasks and require practical change management and motivation. In this book, you will learn how to motivate and inspire people for these new tasks, so that the steps towards using AI in the work environment can succeed in the best possible way. About the Author: Prof. Dr. Andreas Moring is Professor of Digital Business, Innovation & AI at the International School of Management. He is founder and director of the JuS.TECH Institute for AI and Sustainability, co-founder of the WeGoFive initiative for productive human-AI cooperation and topic sponsor for human-AI cooperation at the Artificial Intelligence Center ARIC in Hamburg. This book is a translation of the original German 1st edition KI im Job by Andreas Moring, published by Springer-Verlag GmbH Germany, part of Springer Nature in 2021. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.

Predictive Intelligence for Data-Driven Managers

Author : Uwe Seebacher
Publisher : Springer Nature
ISBN 13 : 3030694038
Total Pages : 275 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Predictive Intelligence for Data-Driven Managers by : Uwe Seebacher

Download or read book Predictive Intelligence for Data-Driven Managers written by Uwe Seebacher and published by Springer Nature. This book was released on 2021-03-26 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes how companies can easily and pragmatically set up and realize the path to a data-driven enterprise, especially in the marketing practice, without external support and additional investments. Using a predictive intelligence (PI) ecosystem, the book first introduces and explains the most important concepts and terminology. The PI maturity model then describes the phases in which you can build a PI ecosystem in your company. The book also demonstrates a PI self-test which helps managers identify the initial steps. In addition, a blueprint for a PI tech stack is defined for the first time, showing how IT can best support the topic. Finally, the PI competency model summarizes all elements into an action model for the company. The entire book is underpinned with practical examples, and case studies show how predictive intelligence, in the spirit of data-driven management, can be used profitably in the short, medium, and long terms.

Handbook on the Ethics of Artificial Intelligence

Author : David J. Gunkel
Publisher : Edward Elgar Publishing
ISBN 13 : 1803926724
Total Pages : 337 pages
Book Rating : 4.8/5 (39 download)

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Book Synopsis Handbook on the Ethics of Artificial Intelligence by : David J. Gunkel

Download or read book Handbook on the Ethics of Artificial Intelligence written by David J. Gunkel and published by Edward Elgar Publishing. This book was released on 2024-07-05 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: This engaging Handbook identifies and critically examines the moral opportunities and challenges typically attributed to artificial intelligence. It provides a comprehensive overview and examination of the most pressing and urgent problems with this technology by drawing on a wide range of analytical methods, traditions, and approaches.

Generative Deep Learning

Author : David Foster
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492041890
Total Pages : 301 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Generative Deep Learning by : David Foster

Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2019-06-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Grokking Machine Learning

Author : Luis Serrano
Publisher : Simon and Schuster
ISBN 13 : 1617295914
Total Pages : 510 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Grokking Machine Learning by : Luis Serrano

Download or read book Grokking Machine Learning written by Luis Serrano and published by Simon and Schuster. This book was released on 2021-12-14 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.

Introduction to Machine Learning

Author : Ethem Alpaydin
Publisher : MIT Press
ISBN 13 : 0262028182
Total Pages : 639 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Introduction to Machine Learning by : Ethem Alpaydin

Download or read book Introduction to Machine Learning written by Ethem Alpaydin and published by MIT Press. This book was released on 2014-08-22 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.

An Introduction to Statistical Learning

Author : Gareth James
Publisher : Springer Nature
ISBN 13 : 3031387473
Total Pages : 617 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis An Introduction to Statistical Learning by : Gareth James

Download or read book An Introduction to Statistical Learning written by Gareth James and published by Springer Nature. This book was released on 2023-08-01 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Supervised Sequence Labelling with Recurrent Neural Networks

Author : Alex Graves
Publisher : Springer
ISBN 13 : 3642247970
Total Pages : 148 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Supervised Sequence Labelling with Recurrent Neural Networks by : Alex Graves

Download or read book Supervised Sequence Labelling with Recurrent Neural Networks written by Alex Graves and published by Springer. This book was released on 2012-02-06 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.

Handbook on Evaluation

Author : Reinhard Stockmann
Publisher : Edward Elgar Publishing
ISBN 13 : 1035321483
Total Pages : 421 pages
Book Rating : 4.0/5 (353 download)

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Book Synopsis Handbook on Evaluation by : Reinhard Stockmann

Download or read book Handbook on Evaluation written by Reinhard Stockmann and published by Edward Elgar Publishing. This book was released on 2024-08-06 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this Handbook, Reinhard Stockmann and other esteemed experts in the field provide a systematic and comprehensive exploration into the planning, process, implementation and utilisation of evaluations. Covering the process and individual steps of evaluation in detail, in chronological order and in terms of practical application, it identifies the characteristics and standards that distinguish a professionally and competently conducted evaluation.

Introduction to Machine Learning

Author : Shan-e-Fatima
Publisher : Blue Rose Publishers
ISBN 13 :
Total Pages : 189 pages
Book Rating : 4./5 ( download)

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Book Synopsis Introduction to Machine Learning by : Shan-e-Fatima

Download or read book Introduction to Machine Learning written by Shan-e-Fatima and published by Blue Rose Publishers. This book was released on 2023-09-25 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the use of machine learning (ML), which is a form of artificial intelligence (AI), software programmers may predict outcomes more accurately without having to be explicitly instructed to do so. In order to forecast new output values, machine learning algorithms use historical data as input. Machine learning is frequently used in recommendation engines. Business process automation (BPA), predictive maintenance, spam filtering, malware threat detection, and fraud detection are a few additional common uses. Machine learning is significant because it aids in the development of new goods and provides businesses with a picture of trends in consumer behavior and operational business patterns. For many businesses, machine learning has emerged as a key competitive differentiation. The fundamental methods of machine learning are covered in the current book.

The Democratization of Artificial Intelligence

Author : Andreas Sudmann
Publisher : transcript Verlag
ISBN 13 : 3839447194
Total Pages : 335 pages
Book Rating : 4.8/5 (394 download)

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Book Synopsis The Democratization of Artificial Intelligence by : Andreas Sudmann

Download or read book The Democratization of Artificial Intelligence written by Andreas Sudmann and published by transcript Verlag. This book was released on 2019-10-31 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms?