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Machine Learning Mit Python Das Praxis Handbuch Fur Data Science Predictive Analytics Und Deep Learning

Machine Learning With Tensorflow
Author: SEBASTIAN RASCHKA.
Publisher:
ISBN: 9783958454231
Size: 57.54 MB
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MACHINE LEARNING MIT PYTHON;DAS PRAXIS-HANDBUCH FUR DATA SCIENCE, PREDICTIVE ANALYTICS UND DEEP LEARNING.
Language: de
Pages:
Authors: SEBASTIAN RASCHKA.
Categories:
Type: BOOK - Published: - Publisher:
Books about MACHINE LEARNING MIT PYTHON;DAS PRAXIS-HANDBUCH FUR DATA SCIENCE, PREDICTIVE ANALYTICS UND DEEP LEARNING.
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
Hands-On Machine Learning with TensorFlow.js
Language: en
Pages: 296
Authors: Kai Sasaki
Categories: Computers
Type: BOOK - Published: 2019-11-27 - Publisher: Packt Publishing Ltd
Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively Key Features Build, train and run machine learning models in the browser using TensorFlow.js Create smart web applications from scratch with the help of useful examples Use flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms function Book Description TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach. Starting with the basics, you'll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The book will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML book, you'll discover useful tips and tricks
Advanced Deep Learning with TensorFlow 2 and Keras
Language: en
Pages: 512
Authors: Rowel Atienza
Categories: Computers
Type: BOOK - Published: 2020-02-28 - Publisher: Packt Publishing Ltd
Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can
Kubernetes in Action
Language: de
Pages: 670
Authors: Marko Lukša
Categories: Computers
Type: BOOK - Published: 2020-05-11 - Publisher: Carl Hanser Verlag GmbH Co KG
Mit Kubernetes große Container-Infrastrukturen ausfallsicher verwalten Nach einer Einführung in die typischen Problemstellungen, mit denen Softwareentwickler und Administratoren konfrontiert sind, und wie diese mit Kubernetes gelöst werden können, lernen Sie in einem ersten Beispielprojekt die praktische Umsetzung. Es wird gezeigt, wie eine einfache in einem Container laufende Web-Applikation über ein Kubernetes-Cluster verwaltet werden kann. Im zweiten Teil des Buches lernen Sie die zu Grunde liegenden Konzepte kennen, deren Verständnis unbedingt notwendig ist, um große Container-Cluster mit Kubernetes zu betreiben. Im letzten Teil wird die Funktionsweise von Kubernetes beschrieben und auf weiterführende Aspekte eingegangen. Hier wird außerdem das erworbene Wissen aus den ersten beiden Teilen zusammengeführt, damit Sie den vollen Nutzen aus der Kubernetes-Plattform ziehen können.
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
Machine Learning With Tensorflow
Language: en
Pages: 128
Authors: Frank Millstein
Categories: Computers
Type: BOOK - Published: 2020-07-03 - Publisher: Frank Millstein
Machine Learning with TensorFlow TensorFlow is a powerful open source software library for performing various numerical data flow graphs. With its powerful resources, TensorFlow is perfect for machine learning enthusiasts offering plenty of workspace where you can improve your machine learning techniques and build your own machine learning algorithms. Thanks to its capability, in recent times TensorFlow definitely has made its way into the software mainstream, so everyone who is interested in machine learnings definitely should considers getting hands on TensorFlow practices. With this book as your guide, you will get your hands on TensorFlow machine learning techniques, learn how to perform different neural network operations, learn how to deal with massive datasets and finally build your first machine learning model for data classification. Here Is a Preview of What You’ll Learn Here… What is machine learning Main uses and benefits of machine learning How to get started with TensorFlow, installing and loading data Data flow graphs and basic TensorFlow expressions How to define your data flow graphs and how to use TensorBoard for data visualization Main TensorFlow operations and building tensors How to perform data transformation using different techniques How to build high performance data pipelines using TensorFlow Dataset
Deep Learning with TensorFlow
Language: en
Pages: 484
Authors: Giancarlo Zaccone, Md. Rezaul Karim
Categories: Computers
Type: BOOK - Published: 2018-03-30 - Publisher: Packt Publishing Ltd
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
Learn TensorFlow 2.0
Language: en
Pages: 164
Authors: Pramod Singh, Avinash Manure
Categories: Computers
Type: BOOK - Published: 2019-12-17 - Publisher: Apress
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. What You'll Learn Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples Who This Book Is For Data scientists, machine and deep learning engineers.
Machine Learning with TensorFlow, Second Edition
Language: en
Pages: 456
Authors: Mattmann A. Chris
Categories: Computers
Type: BOOK - Published: 2021-02-02 - Publisher: Manning Publications
Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using