Introduction Are you interested in using a neural network to generate text? TensorFlow and Keras can be used for some amazing applications of natural language processing techniques, including the generation of text. In this tutorial, we’ll cover the theory behind text generation using a Recurrent Neural Networks, specifically a Long Short-Term Memory Network, implement this network in Python, and use it to generate some text. Defining Terms To begin with, let’s start by defining our terms. It may prove difficult […]

Introduction Generative models are a family of AI architectures whose aim is to create data samples from scratch. They achieve this by capturing the data distributions of the type of things we want to generate. These kind of models are being heavily researched, and there is a huge amount of hype around them. Just look at the chart that shows the numbers of papers published in the field over the past few years: Since 2014, when the first paper on […]

After much hype, Google finally released TensorFlow 2.0 which is the latest version of Google’s flagship deep learning platform. A lot of long-awaited features have been introduced in TensorFlow 2.0. This article very briefly covers how you can develop simple classification and regression models using TensorFlow 2.0. Classification with Tensorflow 2.0 If you have ever worked with Keras library, you are in for a treat. TensorFlow 2.0 now uses Keras API as its default library for training classification and regression […]

This is the 23rd article in my series of articles on Python for NLP. In the previous article of this series, I explained how to perform neural machine translation using seq2seq architecture with Python’s Keras library for deep learning. In this article we will study BERT, which stands for Bidirectional Encoder Representations from Transformers and its application to text classification. BERT is a text representation technique like Word Embeddings. If you have no idea of how word embeddings work, take […]