Natural language processing (NLP) is all over the news lately, and if you ask five different people, you will get ten different definitions. Recently NLP has been used to help identify bots or trolls on the internet trying to spread fake news or, even worse, tactics such as cyberbullying. In fact, recently there was a case in Spain where a student at a school was getting cyberbullied through social media accounts and it was having such a serious effect on the health of the student that the teachers started to get involved. The school reached out to researchers who were able to help identify several potential sources for the trolls using NLP methods such as TF-IDF. Ultimately, the list of potential students was presented to the school and when confronted the actual suspect admitted to being the perpetrator. The story was published in a paper titled Supervised Machine Learning for the Detection of Troll Profiles in Twitter Social Network: Application to a Real Case of Cyberbullying...
Apache Spark Deep Learning Cookbook
By :
Apache Spark Deep Learning Cookbook
By:
Overview of this book
Organizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they’re looking to gain faster and more powerful insights from their data. With this book, you’ll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark.
Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You’ll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning.
By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.
Table of Contents (21 chapters)
Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface
Free Chapter
Setting Up Spark for Deep Learning Development
Creating a Neural Network in Spark
Pain Points of Convolutional Neural Networks
Pain Points of Recurrent Neural Networks
Predicting Fire Department Calls with Spark ML
Using LSTMs in Generative Networks
Natural Language Processing with TF-IDF
Real Estate Value Prediction Using XGBoost
Predicting Apple Stock Market Cost with LSTM
Face Recognition Using Deep Convolutional Networks
Creating and Visualizing Word Vectors Using Word2Vec
Creating a Movie Recommendation Engine with Keras
Image Classification with TensorFlow on Spark
Other Books You May Enjoy
Index
Customer Reviews