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Item #:
50363784
Apache Spark Deep Learning Cookbook: Over 80 recipes that streamline deep learning in a distributed environment with Apache Spark
62% of respondents would recommend this to a friend
Item #:
50363784
ZAR 1228
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What Stands Out
Comprehensive Coverage
Delivers in-depth insights into Apache Spark's capabilities for deep learning, ensuring users grasp complex concepts through practical recipes and real-world applications, ideal for data scientists and engineers.
Hands-on Recipes
Includes practical recipes that guide users step-by-step in implementing deep learning models, making it accessible for both beginners and experienced practitioners looking to enhance their skill set.
Performance Optimization
Focuses on leveraging Spark's distributed computing power to optimize deep learning tasks, helping users achieve faster training times and efficient resource management compared to traditional methods.
Product Details
- A solution-based guide to put your deep learning models into production with the power of Apache SparkKey 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 SparkBook DescriptionWith 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 reusableWho this book is forIf 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 ContentsSetting Up Spark for Deep Learning DevelopmentCreating a Neural Network in SparkPain Points of Convolutional Neural NetworksPain Points of Recurrent Neural NetworksPredicting Fire Department Calls with Spark MLUsing LSTMs in Generative NetworksNatural Language Processing with TF-IDF Real Estate Value Prediction using XGBoostPredicting Apple Stock Market Cost with LSTMFace Recognition using Deep Convolutional NetworksCreating and Visualizing Word Vectors Using Word2Vec Creating a Movie Recommendation Engine with KerasImage Classification with TensorFlow on Spark
| Publisher | Packt Publishing |
| Publication date | July 13, 2018 |
| Language | English |
| Print length | 474 pages |
| ISBN-10 | 1788474228 |
| ISBN-13 | 978-1788474221 |
| Item Weight | 1.78 pounds (810 grams) |
| Dimensions | 7.5 x 1.07 x 9.25 inches (19.1 x 2.7 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to implement deep learning models using Apache Spark efficiently and effectively.
-
Machine Learning Engineers
Perfect for machine learning engineers who want to integrate deep learning techniques with big data processing capabilities.
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Big Data Analysts
Beneficial for big data analysts seeking to enhance their skills in managing and analyzing large datasets with deep learning.
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Beginners
Not suited for beginners who lack foundational knowledge in data science and deep learning concepts.
Product Description
About This Item
Introducing the Apache Spark Deep Learning Cookbook, the ultimate guide for streamlining deep learning in a distributed environment with Apache Spark! Packed with over 80 recipes, this 1st edition cookbook is a must-have for anyone looking to dive into the world of deep learning using Apache Spark. With the increasing popularity of deep learning, it has become essential to leverage efficient tools and frameworks that can handle large-scale data and distributed computing. That's where Apache Spark comes in. This powerful open-source framework provides a seamless integration between deep learning algorithms and big data processing, making it the go-to solution for tackling complex machine learning tasks. In this cookbook, you'll find an extensive collection of recipes that cover a wide range of topics related to deep learning in a distributed environment with Apache Spark.
From getting started with Spark and deep learning integration to building deep learning pipelines and deploying deep learning applications, this book has it all. You'll also find practical examples, tutorials, and use cases that will help you gain hands-on experience with Spark deep learning. Whether you're a seasoned data scientist looking to expand your knowledge or a newcomer interested in exploring the world of deep learning, this cookbook provides all the tools and resources you need to succeed. With Apache Spark Deep Learning Cookbook, you can unlock the full potential of deep learning in a distributed environment and take your machine learning projects to the next level. Don't miss out on this opportunity to master Apache Spark for deep learning.
Get your copy of the Apache Spark Deep Learning Cookbook today and start creating innovative deep learning solutions with ease.
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Intelligence & Semantics Editorial Review
The "Apache Spark Deep Learning Cookbook" emerges as a dual-faceted resource in customer reception, garnering both high praise and scathing criticism. On one hand, a technical reviewer lauds the book for its structured approach to deep learning using Apache Spark. The reviewer highlights the clarity in recipes that guide readers through the complexities of setting up Spark and implementing various neural networks. Noteworthy use cases are presented, such as predicting fire department calls and stock market trends, which showcase the book's relevance and practical application in real-world scenarios. This aspect creates a solid base for both newcomers and experienced practitioners looking to harness machine learning and deep learning using Spark's capabilities. However, the alternative feedback is overwhelmingly negative, marking the book as "total garbage" and "an absolute disgrace." Critics question its value, arguing that the content does not justify its price point and that the information can be easily sourced via online platforms like Google. This stark divide in opinions suggests that while the book may serve as a beneficial guide for some, its effectiveness and worth are heavily contested. In conclusion, the "Apache Spark Deep Learning Cookbook" can be Considered a potentially useful resource for those wanting to explore deep learning with Apache Spark, but it is met with skepticism regarding its practical application and value. **
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Pros
- Clear, problem-solution approach for setting up Spark for deep learning.
- Diverse topics covered, including CNNs, RNNs, LSTMs, and practical use cases.
- Suitable for both beginners and experienced users in machine learning.
Cons
- Significant criticism regarding the overall value and quality.
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