fastText Quick Start Guide

More Information
Learn
  • Create models using the default command line options in fastText
  • Understand the algorithms used in fastText to create word vectors
  • Combine command line text transformation capabilities and the fastText library to implement a training, validation, and prediction pipeline
  • Explore word representation and sentence classification using fastText
  • Use Gensim and spaCy to load the vectors, transform, lemmatize, and perform other NLP tasks efficiently
  • Develop a fastText NLP classifier using popular frameworks, such as Keras, Tensorflow, and PyTorch
About

Facebook's fastText library handles text representation and classification, used for Natural Language Processing (NLP). Most organizations have to deal with enormous amounts of text data on a daily basis, and gaining efficient data insights requires powerful NLP tools such as fastText.

This book is your ideal introduction to fastText. You will learn how to create fastText models from the command line, without the need for complicated code. You will explore the algorithms that fastText is built on and how to use them for word representation and text classification.

Next, you will use fastText in conjunction with other popular libraries and frameworks such as Keras, TensorFlow, and PyTorch.

Finally, you will deploy fastText models to mobile devices. By the end of this book, you will have all the required knowledge to use fastText in your own applications at work or in projects.

Features
  • Introduction to Facebook's fastText library for NLP
  • Perform efficient word representations, sentence classification, vector representation
  • Build better, more scalable solutions for text representation and classification
Page Count 194
Course Length 5 hours 49 minutes
ISBN 9781789130997
Date Of Publication 25 Jul 2018

Authors

Joydeep Bhattacharjee

Joydeep Bhattacharjee is a Principal Engineer who works for Nineleaps Technology Solutions. After graduating from National Institute of Technology at Silchar, he started working in the software industry, where he stumbled upon Python. Through Python, he stumbled upon machine learning. Now he primarily develops intelligent systems that can parse and process data to solve challenging problems at work. He believes in sharing knowledge and loves mentoring in machine learning. He also maintains a machine learning blog on Medium.