Book Image

Hands-On Deep Learning with Apache Spark

By : Guglielmo Iozzia
Book Image

Hands-On Deep Learning with Apache Spark

By: Guglielmo Iozzia

Overview of this book

Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases.
Table of Contents (19 chapters)
Appendix A: Functional Programming in Scala
Appendix B: Image Data Preparation for Spark

Practical applications of DL

The DL concepts and models that were illustrated in the previous two sections aren't just pure theory practical applications have been implemented from them. DL excels at identifying patterns in unstructured data; most use cases are related to media such as images, sound, video, and text. Nowadays, DL is applied in a number of use case scenarios across different business domains, such as the following:

  • Computer vision: A number of applications in the automotive industry, facial recognition, motion detection, and real-time threat detection
  • NLP: Sentiment analysis in social media, fraud detection in finance and insurance, augmented search, and log analysis
  • Medical diagnosis: Anomaly detection, pathology identification
  • Search engines: Image searching
  • IoT: Smart homes, predictive analysis using sensor data
  • Manufacturing: Predictive maintenance...