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Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition - Softcover
Inhaltsangabe
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.
Key Features
- Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow
- Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide
- Gain real-world contextualization through some deep learning problems concerning research and application
Book Description
Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.
This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries.
Throughout the book, you'll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.
You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
What you will learn
- Apply deep machine intelligence and GPU computing with TensorFlow
- Access public datasets and use TensorFlow to load, process, and transform the data
- Discover how to use the high-level TensorFlow API to build more powerful applications
- Use deep learning for scalable object detection and mobile computing
- Train machines quickly to learn from data by exploring reinforcement learning techniques
- Explore active areas of deep learning research and applications
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Über die Autorin bzw. den Autor
Giancarlo Zaccone has over fifteen years' experience of managing research projects in the scientific and industrial domains. He is a software and systems engineer at the European Space Agency (ESTEC), where he mainly deals with the cybersecurity of satellite navigation systems. Giancarlo holds a master's degree in physics and an advanced master's degree in scientific computing. Giancarlo has already authored the following titles, available from Packt: Python Parallel Programming Cookbook (First Edition), Getting Started with TensorFlow, Deep Learning with TensorFlow (First Edition), and Deep Learning with TensorFlow (Second Edition).
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Digital. Zustand: New. Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.About This Book. Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow. Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide. Gain real-world contextualization through some deep learning problems concerning research and applicationWho This Book Is ForThe book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.What You Will Learn. Apply deep machine intelligence and GPU computing with TensorFlow. Access public datasets and use TensorFlow to load, process, and transform the data. Discover how to use the high-level TensorFlow API to build more powerful applications. Use deep learning for scalable object detection and mobile computing. Train machines quickly to learn from data by exploring reinforcement learning techniques. Explore active areas of deep learning research and applicationsIn DetailDeep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries.Throughout the book, you'll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.Style and approachThis step-by-step guide explores common, and not so common, deep neural networks, and shows how they can be exploited in the real world with complex raw data. Benefit from practical examples, and learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing. Bestandsnummer des Verkäufers LU-9781788831109
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Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition
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Packt Publishing Limited, 2018
ISBN 10: 1788831101
ISBN 13: 9781788831109
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Deep Learning with TensorFlow - Second Edition : Explore neural networks and build intelligent systems with Python
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.Key FeaturesLearn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlowExplore deep neural networks and layers of data abstraction with the help of this comprehensive guideGain real-world contextualization through some deep learning problems concerning research and applicationBook DescriptionDeep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries.Throughout the book, you'll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.What you will learnApply deep machine intelligence and GPU computing with TensorFlowAccess public datasets and use TensorFlow to load, process, and transform the dataDiscover how to use the high-level TensorFlow API to build more powerful applicationsUse deep learning for scalable object detection and mobile computingTrain machines quickly to learn from data by exploring reinforcement learning techniquesExplore active areas of deep learning research and applications. Bestandsnummer des Verkäufers 9781788831109
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Deep Learning with TensorFlow -
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Digital. Zustand: New. Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.About This Book. Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow. Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide. Gain real-world contextualization through some deep learning problems concerning research and applicationWho This Book Is ForThe book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.What You Will Learn. Apply deep machine intelligence and GPU computing with TensorFlow. Access public datasets and use TensorFlow to load, process, and transform the data. Discover how to use the high-level TensorFlow API to build more powerful applications. Use deep learning for scalable object detection and mobile computing. Train machines quickly to learn from data by exploring reinforcement learning techniques. Explore active areas of deep learning research and applicationsIn DetailDeep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries.Throughout the book, you'll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.Style and approachThis step-by-step guide explores common, and not so common, deep neural networks, and shows how they can be exploited in the real world with complex raw data. Benefit from practical examples, and learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing. Bestandsnummer des Verkäufers LU-9781788831109
Anzahl: Mehr als 20 verfügbar