Apache Spark Deep Learning Recipes [Video]
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Free ChapterCreating a Neural Network in Spark
- The Course overview
- Creating a Dataframes in Pyspark
- Manipulating Columns in a Pyspark Dataframes
- Converting a PySparkdataframe to an array
- Visualizing an Array in a Scatterplot
- Setting up Weights and Biases for Input into the Neural Network
- Normalizing the Input Data for the Neural Network
- Validating Array for Optimal Neural Network Performance
- Setting up the Activation Function with Sigmoid
- Creating the Sigmoid Derivative Function
- Calculating the Cost Function in a Neural Network
- Predicting Gender based on Height and Weight
- Visualizing Prediction Scores
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Pain Points of Convolutional Neural Networks
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Predicting Fire Department Calls with Spark ML
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Natural Language Processing with TF-IDF
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Predicting Apple Stock Market Cost with LSTM
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.
This video course start offs by explaining the process of developing a neural network from scratch using deep learning libraries such as Tensorflow or Keras. It focuses on the pain points of convolution neural networks. We’ll predict fire department calls with Spark ML and Apple stock market cost with LSTM. We’ll walk you through the steps to classify chatbot conversation data for escalation.
By the end of the video course, you'll have all the basic knowledge about apache spark.
The code bundle for this video course is available at https://github.com/PacktPublishing/Apache-Spark-Deep-Learning-Recipes
Style and Approach
This course includes practical, easy-to-understand solutions on how you can implement the popular deep learning libraries such as TensorFlow and Keras to train your deep learning models on Apache Spark, without getting bogged down in theory.
- Publication date:
- October 2018
- Publisher
- Packt
- Duration
- 1 hour 49 minutes
- ISBN
- 9781789955521