Book Image

Apache Spark Deep Learning Recipes [Video]

By : Ahmed Sherif, Amrith Ravindra
Book Image

Apache Spark Deep Learning Recipes [Video]

By: Ahmed Sherif, Amrith Ravindra

Overview of this book

<p>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. <br />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.<br />By the end of the video course, you'll have all the basic knowledge about apache spark.<br />The code bundle for this video course is available at&nbsp;<a href="https://github.com/PacktPublishing/Apache-Spark-Deep-Learning-Recipes" target="_blank">https://github.com/PacktPublishing/Apache-Spark-Deep-Learning-Recipes</a></p> <h1>Style and Approach</h1> <p>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.</p>
Table of Contents (5 chapters)
Chapter 2
Pain Points of Convolutional Neural Networks
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Section 5
Pain Point #5: Utilizing Alternate Sources for Trained Images
This video will explain to us how the process of transfer learning works as well as when applied to the MNIST dataset. - Study alternative sources for trained images