Apache Spark Deep Learning Cookbook
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.
|Course Length||14 hours 13 minutes|
|Date Of Publication||12 Jul 2018|
|Downloading the San Francisco fire department calls dataset|
|Identifying the target variable of the logistic regression model|
|Preparing feature variables for the logistic regression model|
|Applying the logistic regression model|
|Evaluating the accuracy of the logistic regression model|
|Downloading and loading the MIT-CBCL dataset into the memory|
|Plotting and visualizing images from the directory|
|Model building, training, and analysis|