| Description |
1 online resource (462 pages) |
| Contents |
Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Foreword -- Contributors -- Table of Contents -- Preface -- Chapter 1: Setting Up Spark for Deep Learning Development -- Introduction -- Downloading an Ubuntu Desktop image -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Installing and configuring Ubuntu with VMWare Fusion on macOS -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Installing and configuring Ubuntu with Oracle VirtualBox on Windows -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Installing and configuring Ubuntu Desktop for Google Cloud Platform -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Installing and configuring Spark and prerequisites on Ubuntu Desktop -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Integrating Jupyter notebooks with Spark -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Starting and configuring a Spark cluster -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Stopping a Spark cluster -- How to do it ... -- How it works ... -- There's more ... -- Chapter 2: Creating a Neural Network in Spark -- Introduction -- Creating a dataframe in PySpark -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Manipulating columns in a PySpark dataframe -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- Converting a PySpark dataframe to an array -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Visualizing an array in a scatterplot -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also |
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Setting up weights and biases for input into the neural network -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Normalizing the input data for the neural network -- Getting ready -- How to do it ... -- How it works ... -- See also -- Validating array for optimal neural network performance -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Setting up the activation function with sigmoid -- Getting ready -- How to do it ... -- How it works ... -- See also -- Creating the sigmoid derivative function -- Getting ready -- How to do it ... -- How it works ... -- See also -- Calculating the cost function in a neural network -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Predicting gender based on height and weight -- Getting ready -- How to do it ... -- How it works ... -- See also -- Visualizing prediction scores -- Getting ready -- How to do it ... -- How it works ... -- Pain Points of Convolutional Neural Chapter 3: Networks -- Introduction -- Pain Point #1: Importing MNIST images -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Pain Point #2: Visualizing MNIST images -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Pain Point #3: Exporting MNIST images as files -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Pain Point #4: Augmenting MNIST images -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Pain Point #5: Utilizing alternate sources for trained images -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Pain Point #6: Prioritizing high-level libraries for CNNs -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also |
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Chapter 4: Pain Points of Recurrent Neural Networks -- Introduction -- Introduction to feedforward networks -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Sequential workings of RNNs -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Pain point #1 -- The vanishing gradient problem -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Pain point #2 -- The exploding gradient problem -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Sequential working of LSTMs -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Chapter 5: Predicting Fire Department Calls with Spark ML -- Introduction -- Downloading the San Francisco fire department calls dataset -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Identifying the target variable of the logistic regression model -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Preparing feature variables for the logistic regression model -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Applying the logistic regression model -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Evaluating the accuracy of the logistic regression model -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Chapter 6: Using LSTMs in Generative Networks -- Introduction -- Downloading novels/books that will be used as input text -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Preparing and cleansing data -- Getting ready -- How to do it ... -- How it works ... -- See also -- Tokenizing sentences -- Getting ready -- How to do it .. |
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How it works ... -- There's more ... -- Training and saving the LSTM model -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Generating similar text using the model -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Chapter 7: Natural Language Processing with TF-IDF -- Introduction -- Downloading the therapy bot session text dataset -- Getting ready -- How it works ... -- How to do it ... -- There's more ... -- Analyzing the therapy bot session dataset -- Getting ready -- How to do it ... -- How it works ... -- Visualizing word counts in the dataset -- Getting ready -- How to do it ... -- How it works ... -- See also -- Calculating sentiment analysis of text -- Getting ready -- How to do it ... -- How it works ... -- See also -- Removing stop words from the text -- Getting ready -- How to do it ... -- How it works ... -- See also -- Training the TF-IDF model -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Evaluating TF-IDF model performance -- Getting ready -- How to do it ... -- How it works ... -- See also -- Comparing model performance to a baseline score -- How to do it ... -- How it works ... -- See also -- Chapter 8: Real Estate Value Prediction Using XGBoost -- Downloading the King County House sales dataset -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Performing exploratory analysis and visualization -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Plotting correlation between price and other features -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Predicting the price of a house -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also |
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Chapter 9: Predicting Apple Stock Market Cost with LSTM -- Downloading stock market data for Apple -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Exploring and visualizing stock market data for Apple -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Preparing stock data for model performance -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Building the LSTM model -- Getting ready -- How to do it ... -- How it works ... -- See also -- Evaluating the model -- Getting ready -- How to do it ... -- How it works ... -- See also -- Chapter 10: Face Recognition Using Deep Convolutional Networks -- Introduction -- Downloading and loading the MIT-CBCL dataset into the memory -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Plotting and visualizing images from the directory -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Preprocessing images -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Model building, training, and analysis -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Chapter 11: Creating and Visualizing Word Vectors Using Word2Vec -- Introduction -- Acquiring data -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Importing the necessary libraries -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Preparing the data -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Building and training the model -- Getting ready -- How to do it ... -- How it works ... -- There's more ... -- See also -- Visualizing further -- Getting ready -- How to do it ... -- How it works .. |
| Summary |
Apache Spark Deep Learning Cookbook presents useful tips and tricks to overcome any problem related to building efficient distributed deep learning applications on Apache Spark. With the help of this book, you will leverage powerful deep learning libraries such as TensorFlow to develop your models and ensure their optimum performance |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from READ title page (OverDrive, viewed November 9, 2018) |
| SUBJECT |
Spark (Electronic resource : Apache Software Foundation) http://id.loc.gov/authorities/names/no2015027445
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Spark (Electronic resource : Apache Software Foundation) fast |
| Subject |
Machine learning.
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Electronic data processing -- Distributed processing.
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Information visualization.
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Electronic data processing -- Distributed processing
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Information visualization
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Machine learning
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| Form |
Electronic book
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| Author |
Ravindra, Amrith
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| ISBN |
9781788471558 |
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1788471555 |
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9781788474221 |
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1788474228 |
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