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Produkt zum Begriff Deep Learning:


  • Ekman, Magnus: Learning Deep Learning
    Ekman, Magnus: Learning Deep Learning

    Learning Deep Learning , NVIDIA's Full-Color Guide to Deep Learning: All StudentsNeed to Get Started and Get Results Learning Deep Learning is a complete guide to DL.Illuminating both the core concepts and the hands-on programming techniquesneeded to succeed, this book suits seasoned developers, data scientists,analysts, but also those with no prior machine learning or statisticsexperience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers,Magnus Ekman shows how to use them to build advanced architectures, includingthe Transformer. He describes how these concepts are used to build modernnetworks for computer vision and natural language processing (NLP), includingMask R-CNN, GPT, and BERT. And he explains how a natural language translatorand a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples usingTensorFlow with Keras. Corresponding PyTorch examples are provided online, andthe book thereby covers the two dominating Python libraries for DL used inindustry and academia. He concludes with an introduction to neural architecturesearch (NAS), exploring important ethical issues and providing resources forfurther learning. Exploreand master core concepts: perceptrons, gradient-based learning, sigmoidneurons, and back propagation See how DL frameworks make it easier to developmore complicated and useful neural networks Discover how convolutional neuralnetworks (CNNs) revolutionize image classification and analysis Apply recurrentneural networks (RNNs) and long short-term memory (LSTM) to text and othervariable-length sequences Master NLP with sequence-to-sequence networks and theTransformer architecture Build applications for natural language translation andimage captioning , >

    Preis: 49.28 € | Versand*: 0 €
  • Evolutionary Deep Learning
    Evolutionary Deep Learning

    Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning's common pitfalls and deliver adaptable model upgrades without constant manual adjustment.In Evolutionary Deep Learning you will learn how to:Solve complex design and analysis problems with evolutionary computationTune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimizationUse unsupervised learning with a deep learning autoencoder to regenerate sample dataUnderstand the basics of reinforcement learning and the Q Learning equationApply Q Learning to deep learning to produce deep reinforcement learningOptimize the loss function and network architecture of unsupervised autoencodersMake an evolutionary agent that can play an OpenAI Gym gameEvolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser-known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning.about the technologyEvolutionary deep learning merges the biology-simulating practices of evolutionary computation (EC) with the neural networks of deep learning. This unique approach can automate entire DL systems and help uncover new strategies and architectures. It gives new and aspiring AI engineers a set of optimization tools that can reliably improve output without demanding an endless churn of new data.about the readerFor data scientists who know Python. 

    Preis: 56.7 € | Versand*: 0 €
  • Engineering Deep Learning Systems
    Engineering Deep Learning Systems

    Design systems optimized for deep learning models. Written for software engineers, this book teaches you how to implement a maintainable platform for developing deep learning models.In Engineering Deep Learning Systems you will learn how to:Transfer your software development skills to deep learning systemsRecognize and solve common engineering challenges for deep learning systemsUnderstand the deep learning development cycleAutomate training for models in TensorFlow and PyTorchOptimize dataset management, training, model serving and hyperparameter tuningPick the right open-source project for your platformEngineering Deep Learning Systems is a practical guide for software engineers and data scientists who are designing and building platforms for deep learning. It's full of hands-on examples that will help you transfer your software development skills to implementing deep learning platforms. You'll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an excitingand lucrativecareer as a deep learning engineer.about the technologyBehind every deep learning researcher is a team of engineers bringing their models to production. To build these systems, you need to understand how a deep learning system's platform differs from other distributed systems. By mastering the core ideas in this book, you'll be able to support deep learning systems in a way that's fast, repeatable, and reliable.

    Preis: 56.7 € | Versand*: 0 €
  • Deep Learning Design Patterns
    Deep Learning Design Patterns

    Deep learning has revealed ways to create algorithms for applications that we never dreamed were possible. For software developers, the challenge lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Design Patterns is here to help. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Written by Google deep learning expert Andrew Ferlitsch, it's filled with the latest deep learning insights and best practices from his work with Google Cloud AI. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples.about the technologyYou don't need to design your deep learning applications from scratch! By viewing cutting-edge deep learning models as design patterns, developers can speed up their creation of AI models and improve model understandability for both themselves and other users.about the bookDeep Learning Design Patterns distills models from the latest research papers into practical design patterns applicable to enterprise AI projects. Using diagrams, code samples, and easy-to-understand language, Google Cloud AI expert Andrew Ferlitsch shares insights from state-of-the-art neural networks. You'll learn how to integrate design patterns into deep learning systems from some amazing examples, including a real-estate program that can evaluate house prices just from uploaded photos and a speaking AI capable of delivering live sports broadcasting. Building on your existing deep learning knowledge, you'll quickly learn to incorporate the very latest models and techniques into your apps as idiomatic, composable, and reusable design patterns. what's insideInternal functioning of modern convolutional neural networksProcedural reuse design pattern for CNN architecturesModels for mobile and IoT devicesComposable design pattern for automatic learning methodsAssembling large-scale model deploymentsComplete code samples and example notebooksAccompanying YouTube videosabout the readerFor machine learning engineers familiar with Python and deep learning.about the authorAndrew Ferlitsch is an expert on computer vision and deep learning at Google Cloud AI Developer Relations. He was formerly a principal research scientist for 20 years at Sharp Corporation of Japan, where he amassed 115 US patents and worked on emerging technologies in telepresence, augmented reality, digital signage, and autonomous vehicles. In his present role, he reaches out to developer communities, corporations and universities, teaching deep learning and evangelizing Google's AI technologies.

    Preis: 58.84 € | Versand*: 0 €
  • Was sind die Einsatzmöglichkeiten von Deep Learning in der heutigen Technologie?

    Deep Learning wird in der Bild- und Spracherkennung, automatisierten Übersetzungen und personalisierten Empfehlungssystemen eingesetzt. Es wird auch in der medizinischen Diagnose, autonomen Fahrzeugen und der Finanzanalyse verwendet. Deep Learning hat das Potenzial, viele Bereiche wie Gesundheitswesen, Verkehr und Finanzen zu revolutionieren.

  • Was sind die vielversprechendsten Anwendungen von Deep Learning in der heutigen Technologiebranche?

    Die vielversprechendsten Anwendungen von Deep Learning in der Technologiebranche sind autonomes Fahren, Spracherkennung und -übersetzung sowie personalisierte Empfehlungssysteme. Diese Anwendungen nutzen komplexe neuronale Netzwerke, um Muster in großen Datenmengen zu erkennen und Vorhersagen zu treffen. Deep Learning hat das Potenzial, die Effizienz und Genauigkeit vieler Technologien zu verbessern und neue innovative Lösungen zu schaffen.

  • Was sind die grundlegenden Prinzipien des Deep Learning und wie wird diese Technologie in verschiedenen Anwendungsgebieten eingesetzt?

    Die grundlegenden Prinzipien des Deep Learning beinhalten die Verwendung von künstlichen neuronalen Netzen, um komplexe Muster in Daten zu erkennen und zu lernen. Diese Technologie wird in verschiedenen Anwendungsgebieten wie Bilderkennung, Spracherkennung, maschinelles Übersetzen und autonomes Fahren eingesetzt. Durch die kontinuierliche Optimierung der Netzwerke können Deep Learning-Modelle immer präzisere Ergebnisse liefern.

  • Wie kann Deep Learning in der Medizin eingesetzt werden, um Krankheiten frühzeitig zu erkennen und die Diagnosegenauigkeit zu verbessern?

    Deep Learning kann in der Medizin eingesetzt werden, um große Mengen von medizinischen Bildern, wie zum Beispiel MRT-Scans oder Röntgenaufnahmen, zu analysieren und dabei frühzeitig Anzeichen von Krankheiten zu erkennen. Durch die Verwendung von Deep Learning-Algorithmen können auch komplexe Muster und Zusammenhänge in den Daten identifiziert werden, was zu einer verbesserten Diagnosegenauigkeit führt. Zudem ermöglicht Deep Learning die Entwicklung von personalisierten Medizinansätzen, indem es die individuellen Risikofaktoren und Behandlungsoptionen für Patienten berücksichtigt. Durch die kontinuierliche Analyse von Patientendaten kann Deep Learning auch dazu beitragen, Krankheitsverläufe besser zu verstehen und präventive Maßnahmen zu entwickeln.

Ähnliche Suchbegriffe für Deep Learning:


  • Deep Learning with Python
    Deep Learning with Python

    "The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd CookThe bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher François Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You'll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable.about the technologyMachine learning has made remarkable progress in recent years. We've gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn't beat a serious Go player, to defeating a world champion. Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learninga combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sectorabout the bookDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You'll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.what's insideDeep learning from first principlesImage-classification, imagine segmentation, and object detectionDeep learning for natural language processingTimeseries forecastingNeural style transfer, text generation, and image generationabout the readerReaders need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.about the authorFrançois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does AI research, with a focus on abstraction and reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

    Preis: 58.84 € | Versand*: 0 €
  • Bishop, Christopher M.: Deep Learning
    Bishop, Christopher M.: Deep Learning

    Deep Learning , This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time. The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code. Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University. ¿Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.¿ -- Geoffrey Hinton "With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." ¿ Yann LeCun ¿This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.¿ -- Yoshua Bengio , Studium & Erwachsenenbildung > Fachbücher, Lernen & Nachschlagen

    Preis: 75.37 € | Versand*: 0 €
  • Inside Deep Learning: Math, Algorithms, Models
    Inside Deep Learning: Math, Algorithms, Models

    "If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!" - Tiklu GangulyJourney through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems.In Inside Deep Learning, you will learn how to:Implement deep learning with PyTorchSelect the right deep learning componentsTrain and evaluate a deep learning modelFine tune deep learning models to maximize performanceUnderstand deep learning terminologyAdapt existing PyTorch code to solve new problemsInside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skippedyou'll dive into math, theory, and practical applications. Everything is clearly explained in plain English.about the technologyDeep learning isn't just for big tech companies and academics. Anyone who needs to find meaningful insights and patterns in their data can benefit from these practical techniques! The unique ability for your systems to learn by example makes deep learning widely applicable across industries and use-cases, from filtering out spam to driving cars.about the bookInside Deep Learning is a fast-paced beginners' guide to solving common technical problems with deep learning. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory. You'll learn how deep learning works through plain language, annotated code and equations as you work through dozens of instantly useful PyTorch examples.As you go, you'll build a French-English translator that works on the same principles as professional machine translation and discover cutting-edge techniques just emerging from the latest research. Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware!about the readerFor Python programmers with basic machine learning skills.about the authorEdward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.

    Preis: 49.21 € | Versand*: 0 €
  • Math and Architectures of Deep Learning
    Math and Architectures of Deep Learning

    The mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you'll peer inside the black box to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.about the technologyIt's important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You'll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you'll be glad you can quickly identify and fix problems.about the bookMath and Architectures of Deep Learning sets out the foundations of DL in a way that's both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You'll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you're done, you'll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge.

    Preis: 47.07 € | Versand*: 0 €
  • Kann ich damit Deep-Sky fotografieren?

    Ja, du kannst mit einer geeigneten Kamera und einem Teleskop Deep-Sky-Fotografie betreiben. Du benötigst jedoch auch eine Nachführung, um die Bewegung der Himmelsobjekte auszugleichen, da sie sich über längere Belichtungszeiten hinweg bewegen. Es ist auch wichtig, an einem dunklen Ort zu sein, um Lichtverschmutzung zu minimieren.

  • Was ist das Besondere an Deep Purple?

    Deep Purple ist eine britische Rockband, die für ihren einzigartigen und innovativen Sound bekannt ist. Sie waren eine der ersten Bands, die Elemente des Hard Rock und des Heavy Metal in ihre Musik integrierten. Ihre markanten Gitarrenriffs, das virtuose Keyboardspiel und die kraftvolle Gesangsleistung haben sie zu einer der einflussreichsten Bands der Rockgeschichte gemacht.

  • Was ist "Deep Talk 3"?

    "Deep Talk 3" ist ein Begriff, der verwendet wird, um eine tiefgründige und bedeutungsvolle Unterhaltung zu beschreiben. Es kann sich um ein Gespräch handeln, das auf persönlichen Erfahrungen, Emotionen oder philosophischen Themen basiert. "Deep Talk 3" kann auch als Titel für eine Veranstaltung oder eine Serie von Gesprächen verwendet werden, die darauf abzielen, eine tiefe Verbindung und ein Verständnis zwischen den Teilnehmern herzustellen.

  • Repostet ein Freund Deep TikToks?

    Ja, es ist möglich, dass ein Freund Deep TikToks repostet. Es könnte bedeuten, dass er Interesse an diesem speziellen TikTok-Genre hat oder dass er es lustig oder interessant findet und es mit anderen teilen möchte. Es könnte auch bedeuten, dass er die Kreativität und Originalität dieser Art von TikToks schätzt.

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