Convolutional Neural Networks With Swift For Tensorflow


Convolutional Neural Networks With Swift For Tensorflow
Author: Brett Koonce
Publisher: Apress
ISBN: 9781484261675
Size: 66.98 MB
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Convolutional Neural Networks With Swift For Tensorflow

eBook File: Convolutional-neural-networks-with-swift-for-tensorflow.PDF Book by Brett Koonce, Convolutional Neural Networks With Swift For Tensorflow Books available in PDF, EPUB, Mobi Format. Download Convolutional Neural Networks With Swift For Tensorflow books, Dive into and apply practical machine learning and dataset categorization techniques while learning Tensorflow and deep learning. This book uses convolutional neural networks to do image recognition all in the familiar and easy to work with Swift language. It begins with a basic machine learning overview and then ramps up to neural networks and convolutions and how they work. Using Swift and Tensorflow, you'll perform data augmentation, build and train large networks, and build networks for mobile devices. You’ll also cover cloud training and the network you build can categorize greyscale data, such as mnist, to large scale modern approaches that can categorize large datasets, such as imagenet. Convolutional Neural Networks with Swift for Tensorflow uses a simple approach that adds progressive layers of complexity until you have arrived at the current state of the art for this field. What You'll Learn Categorize and augment datasets Build and train large networks, including via cloud solutions Deploy complex systems to mobile devices Who This Book Is For Developers with Swift programming experience who would like to learn convolutional neural networks by example using Swift for Tensorflow as a starting point.


Convolutional Neural Networks with Swift for Tensorflow
Language: en
Pages: 245
Authors: Brett Koonce
Categories: Computers
Type: BOOK - Published: 2021-01-05 - Publisher: Apress
Dive into and apply practical machine learning and dataset categorization techniques while learning Tensorflow and deep learning. This book uses convolutional neural networks to do image recognition all in the familiar and easy to work with Swift language. It begins with a basic machine learning overview and then ramps up to neural networks and convolutions and how they work. Using Swift and Tensorflow, you'll perform data augmentation, build and train large networks, and build networks for mobile devices. You’ll also cover cloud training and the network you build can categorize greyscale data, such as mnist, to large scale modern approaches that can categorize large datasets, such as imagenet. Convolutional Neural Networks with Swift for Tensorflow uses a simple approach that adds progressive layers of complexity until you have arrived at the current state of the art for this field. What You'll Learn Categorize and augment datasets Build and train large networks, including via cloud solutions Deploy complex systems to mobile devices Who This Book Is For Developers with Swift programming experience who would like to learn convolutional neural networks by example using Swift for Tensorflow as a starting point.
Practical Artificial Intelligence with Swift
Language: en
Pages: 526
Authors: Mars Geldard, Jonathon Manning, Paris Buttfield-Addison, Tim Nugent
Categories: Computers
Type: BOOK - Published: 2019-09-03 - Publisher: O'Reilly Media
Create and implement AI-based features in your Swift apps for iOS, macOS, tvOS, and watchOS. With this practical book, programmers and developers of all kinds will find a one-stop shop for AI and machine learning with Swift. Taking a task-based approach, you’ll learn how to build features that use powerful AI features to identify images, make predictions, generate content, recommend things, and more. AI is increasingly essential for every developer—and you don’t need to be a data scientist or mathematician to take advantage of it in your apps. Explore Swift-based AI and ML techniques for building applications. Learn where and how AI-driven features make sense. Inspect tools such as Apple’s Python-powered Turi Create and Google’s Swift for TensorFlow to train and build models. I: Fundamentals and Tools—Learn AI basics, our task-based approach, and discover how to build or find a dataset. II: Task Based AI—Build vision, audio, text, motion, and augmentation-related features; learn how to convert preexisting models. III: Beyond—Discover the theory behind task-based practice, explore AI and ML methods, and learn how you can build it all from scratch... if you want to
TensorFlow für Dummies
Language: de
Pages: 324
Authors: Matthew Scarpino
Categories: Computers
Type: BOOK - Published: 2018-11-19 - Publisher: John Wiley & Sons
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.
Deep Learning mit Python und Keras
Language: de
Pages: 447
Authors: Chollet, François
Categories: Computers
Type: BOOK - Published: 2018-05-24 - Publisher: MITP-Verlags GmbH & Co. KG
Books about Deep Learning mit Python und Keras
Biomedical Signal Processing and Artificial Intelligence in Healthcare
Language: en
Pages: 268
Authors: Walid A. Zgallai
Categories: Technology & Engineering
Type: BOOK - Published: 2020-07-29 - Publisher: Academic Press
Biomedical Signal Processing and Artificial Intelligence in Healthcare is a new volume in the Developments in Biomedical Engineering and Bioelectronics series. This volume covers the basics of biomedical signal processing and artificial intelligence. It explains the role of machine learning in relation to processing biomedical signals and the applications in medicine and healthcare. The book provides background to statistical analysis in biomedical systems. Several types of biomedical signals are introduced and analyzed, including ECG and EEG signals. The role of Deep Learning, Neural Networks, and the implications of the expansion of artificial intelligence is covered. Biomedical Images are also introduced and processed, including segmentation, classification, and detection. This book covers different aspects of signals, from the use of hardware and software, and making use of artificial intelligence in problem solving. Dr Zgallai’s book has up to date coverage where readers can find the latest information, easily explained, with clear examples and illustrations. The book includes examples on the application of signal and image processing employing artificial intelligence to Alzheimer, Parkinson, ADHD, autism, and sleep disorders, as well as ECG and EEG signals. Developments in Biomedical Engineering and Bioelectronics is a 10-volume series which covers recent developments, trends and advances in
MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities
Language: en
Pages: 181
Authors: Wu, Jiann-Ming, Tien, Chao-Yuan
Categories: Computers
Type: BOOK - Published: 2020-04-17 - Publisher: IGI Global
Deep learning has become a trending area of research due to its adaptive characteristics and high levels of applicability. In recent years, researchers have begun applying deep learning strategies to image analysis and pattern recognition for solving technical issues within image classification. As these technologies continue to advance, professionals have begun translating this intelligent programming language into mobile applications for devices. Programmers and web developers are in need of significant research on how to successfully develop pattern recognition applications using intelligent programming. MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities is an essential reference source that presents a solution to developing intelligent pattern recognition Apps on iOS devices based on MatConvNet deep learning. Featuring research on topics such as medical image diagnosis, convolutional neural networks, and character classification, this book is ideally designed for programmers, developers, researchers, practitioners, engineers, academicians, students, scientists, and educators seeking coverage on the specific development of iOS mobile applications using pattern recognition strategies.
Deep Learning mit TensorFlow, Keras und TensorFlow.js
Language: de
Pages: 456
Authors: Matthieu Deru, Alassane Ndiaye
Categories: Computers
Type: BOOK - Published: 2020-03-20 - Publisher:
Books about Deep Learning mit TensorFlow, Keras und TensorFlow.js
Deep Learning with TensorFlow 2 and Keras
Language: en
Pages: 646
Authors: Antonio Gulli, Amita Kapoor, Sujit Pal
Categories: Computers
Type: BOOK - Published: 2019-12-27 - Publisher: Packt Publishing Ltd
Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key Features Introduces and then uses TensorFlow 2 and Keras right from the start Teaches key machine and deep learning techniques Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples Book Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learn Build machine learning and deep learning systems with TensorFlow 2 and the Keras API Use Regression analysis, the
Einführung in Machine Learning mit Python
Language: de
Pages: 378
Authors: Andreas C. Müller, Sarah Guido
Categories: Computers
Type: BOOK - Published: 2017-07-21 - Publisher: O'Reilly
Machine Learning ist zu einem wichtigen Bestandteil vieler kommerzieller Anwendungen und Forschungsprojekte geworden, von der medizinischen Diagnostik bis hin zur Suche nach Freunden in sozialen Netzwerken. Um Machine-Learning-Anwendungen zu entwickeln, braucht es keine großen Expertenteams: Wenn Sie Python-Grundkenntnisse mitbringen, zeigt Ihnen dieses Praxisbuch, wie Sie Ihre eigenen Machine-Learning-Lösungen erstellen. Mit Python und der scikit-learn-Bibliothek erarbeiten Sie sich alle Schritte, die für eine erfolgreiche Machine-Learning-Anwendung notwendig sind. Die Autoren Andreas Müller und Sarah Guido konzentrieren sich bei der Verwendung von Machine-Learning-Algorithmen auf die praktischen Aspekte statt auf die Mathematik dahinter. Wenn Sie zusätzlich mit den Bibliotheken NumPy und matplotlib vertraut sind, hilft Ihnen dies, noch mehr aus diesem Tutorial herauszuholen. Das Buch zeigt Ihnen: - grundlegende Konzepte und Anwendungen von Machine Learning - Vor- und Nachteile weit verbreiteter maschineller Lernalgorithmen - wie sich die von Machine Learning verarbeiteten Daten repräsentieren lassen und auf welche Aspekte der Daten Sie sich konzentrieren sollten - fortgeschrittene Methoden zur Auswertung von Modellen und zum Optimieren von Parametern - das Konzept von Pipelines, mit denen Modelle verkettet und Arbeitsabläufe gekapselt werden - Arbeitsmethoden für Textdaten, insbesondere textspezifische Verarbeitungstechniken - Möglichkeiten zur Verbesserung Ihrer Fähigkeiten in den Bereichen Machine Learning und Data Science Dieses Buch ist eine
Deep Learning Kochbuch
Language: de
Pages: 262
Authors: Douwe Osinga
Categories: Computers
Type: BOOK - Published: 2019-02-11 - Publisher: O'Reilly
Lassen Sie sich von Deep Learning nicht abschrecken! Dank Frameworks wie Keras und TensorFlow ist der schnelle Einstieg in die Entwicklung von Deep-Learning-Anwendungen nun auch für Softwareentwickler ohne umfassende Machine-Learning-Kenntnisse möglich. Mit den Rezepten aus diesem Buch lernen Sie, typische Aufgabenstellungen des Deep Learning zu lösen, wie etwa die Klassifizierung und Generierung von Texten, Bildern und Musik. Jedes Kapitel behandelt ein Projekt, wie z.B. das Trainieren eines Empfehlungssystems für Musik. Schritt für Schritt wird gezeigt, wie das jeweilige Projekt umgesetzt wird. Darüber hinaus beschreibt der Autor Douwe Osinga zahlreiche Techniken, die Ihnen helfen, wenn Sie einmal nicht mehr weiterwissen. Alle Codebeispiele sind in Python geschrieben und auf GitHub als Python-Notebooks frei verfügbar. Aus dem Inhalt: Entwickeln Sie Deep-Learning-Anwendungen, die Nutzern einen echten Mehrwert bieten Berechnen Sie Ähnlichkeiten von Texten mithilfe von Word-Embeddings Erstellen Sie ein Empfehlungssystem für Filme basierend auf Wikipedia-Links Visualisieren Sie die internen Vorgänge einer künstlichen Intelligenz, um nachvollziehen zu können, wie diese arbeitet Entwickeln Sie ein Modell, das passende Emojis für Textpassagen vorschlägt Realisieren Sie einen Reverse-Image-Search-Dienst mithilfe von vortrainierten Netzwerken Vergleichen Sie, wie Generative Adversarial Networks, Autoencoder und LSTM-Netzwerke Icons erzeugen Trainieren Sie ein Klassifikationsmodell für Musikstile und lassen Sie es Musikstücke dementsprechend zuordnen