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Keras Deep Learning Cookbook: Over 30 recipes for implementing deep neural networks in Python
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Add to basketPaperback. Condition: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less 2.27.
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Neural Network Programming with TensorFlow
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Neural Network Programming with TensorFlow: Unleash the power of TensorFlow to train efficient neural networks
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Neural Network Programming with TensorFlow
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Machine Learning with Spark, Second Edition
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Add to basketCondition: New. Neural Network Programming with TensorFlow (Paperback or Softback) 1.05.
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Machine Learning with Spark - Second Edition
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Neural Network Programming with TensorFlow
Published by Packt Publishing Limited, GB, 2017
ISBN 10: 1788390393 ISBN 13: 9781788390392
Language: English
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Add to basketPaperback. Condition: New. Neural Networks and their implementation decoded with TensorFlowAbout This Book. Develop a strong background in neural network programming from scratch, using the popular Tensorflow library. Use Tensorflow to implement different kinds of neural networks - from simple feedforward neural networks to multilayered perceptrons, CNNs, RNNs and more. A highly practical guide including real-world datasets and use-cases to simplify your understanding of neural networks and their implementation.Who This Book Is ForThis book is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, book can be used as a generic resource to bridge the gap between the math and the implementation of deep learning. If you have some understanding of Tensorflow and Python and want to learn what happens at a level lower than the plain API syntax, this book is for you.What You Will Learn. Learn Linear Algebra and mathematics behind neural network. Dive deep into Neural networks from the basic to advanced concepts like CNN, RNN Deep Belief Networks, Deep Feedforward Networks. Explore Optimization techniques for solving problems like Local minima, Global minima, Saddle points. Learn through real world examples like Sentiment Analysis. Train different types of generative models and explore autoencoders. Explore TensorFlow as an example of deep learning implementation.In DetailIf you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that.You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders.By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow con.
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Keras Deep Learning Cookbook
Published by Packt Publishing, United Kingdom, 2018
ISBN 10: 1788621751 ISBN 13: 9781788621755
Language: English
Seller: True Oak Books, Highland, NY, U.S.A.
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Add to basketPaperback. Condition: Very Good+. First Edition. 238 pages; Very Good condition. No noteworthy defects. No markings. ; - Your satisfaction is our priority. We offer free returns and respond promptly to all inquiries. Your item will be carefully cushioned in bubble wrap and securely boxed. All orders ship on the same or next business day. Buy with confidence.
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Machine Learning with Spark, Second Edition
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Add to basketCondition: New. This book gives you a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning. It presents a unique problem-solution approach to tackle various problems in training different types of neural.
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Add to basketCondition: New. Idioma/Language: Inglés. Create scalable machine learning applications to power a modern data-driven business using Spark 2. xAbout This Book* Get to the grips with the latest version of Apache Spark* Utilize Spark's machine learning library to implement predictive analytics* Leverage Spark's powerful tools to load, analyze, clean, and transform your dataWho This Book Is ForIf you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages. What You Will Learn* Get hands-on with the latest version of Spark ML* Create your first Spark program with Scala and Python* Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2* Access public machine learning datasets and use Spark to load, process, clean, and transform data* Use Spark's machine learning library to implement programs by utilizing well-known machine learning models* Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models* Write Spark functions to evaluate the performance of your machine learning modelsIn DetailThis book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business. Style and approachThis practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system. *** Nota: Los envíos a España peninsular, Baleares y Canarias se realizan a través de mensajería urgente. No aceptamos pedidos con destino a Ceuta y Melilla.
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Add to basketCondition: New. Über den AutorrnrnMohankumar Saraswatipura is a database solutions architect focusing on IBM Db2, Linux, Unix, Windows, and SAP HANA solutions. He is an IBM Champion (2010-2018) and a DB2 s Got Talent 2013 winner. He is also a frequent spea.
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Neural Network Programming with TensorFlow: Unleash the power of TensorFlow to train efficient neural networks: Implementing Multilayered Perceptrons, CNN, RNN and more using Tensorflow
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Machine Learning with Spark - Second Edition: Develop intelligent, distributed machine learning systems
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