Apache Spark Deep Learning Cookbook
Idioma: inglés
Editorial: Packt Publishing, 2018
- Tapa blanda
- Nuevo
Librería: preigu, Osnabrück, Alemaniapreigu
Vendedor de AbeBooks desde el 5 de agosto de 2024
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Apache Spark Deep Learning Cookbook | Ahmed Sherif (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2018 | Packt Publishing | EAN 9781788474221 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
N° de ref. del artículo 114122634
- Título
- Apache Spark Deep Learning Cookbook
- Autor
- Ahmed Sherif (u. a.)
- Editorial
- Packt Publishing
- Año de publicación
- 2018
- Estado
- Neu
- Encuadernación
- Taschenbuch
- Idioma
- inglés
- ISBN 10
- 1788474228
- ISBN 13
- 9781788474221
- Peso del artículo
- 877 gramos
- Dimensiones
- 235 x 191 x 26 mm
- Catálogos de vendedores
- Bücher
A solution-based guide to put your deep learning models into production with the power of Apache Spark
Key Features
- Discover practical recipes for distributed deep learning with Apache Spark
- Learn to use libraries such as Keras and TensorFlow
- Solve problems in order to train your deep learning models on Apache Spark
Book Description
With deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed.
With the help of the Apache Spark Deep Learning Cookbook, you’ll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you’ll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you’ll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras.
By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark.
What you will learn
- Set up a fully functional Spark environment
- Understand practical machine learning and deep learning concepts
- Apply built-in machine learning libraries within Spark
- Explore libraries that are compatible with TensorFlow and Keras
- Explore NLP models such as Word2vec and TF-IDF on Spark
- Organize dataframes for deep learning evaluation
- Apply testing and training modeling to ensure accuracy
- Access readily available code that may be reusable
Who this book is for
If you’re looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python is a plus.
Table of Contents
- Setting Up Spark for Deep Learning Development
- Creating a Neural Network in Spark
- Pain Points of Convolutional Neural Networks
- Pain Points of Recurrent Neural Networks
- Predicting Fire Department Calls with Spark ML
- Using LSTMs in Generative Networks
- Natural Language Processing with TF-IDF
- Real Estate Value Prediction using XGBoost
- Predicting Apple Stock Market Cost with LSTM
- Face Recognition using Deep Convolutional Networks
- Creating and Visualizing Word Vectors Using Word2Vec
- Creating a Movie Recommendation Engine with Keras
- Image Classification with TensorFlow on Spark
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preigu
Osnabrück, Alemania
Vendedor de AbeBooks desde el 5 de agosto de 2024
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| Artículo | De 60 a 60 días hábiles | De 60 a 60 días hábiles |
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