In this tutorial, we will build a text classifier model using RNNs using Tensorflow and Keras in Python. Building deep learning models (using embedding and recurrent layers) for different text classification problems such as sentiment analysis or 20 news group classification using Tensorflow and Keras in Python
Building deep learning models (using embedding and recurrent layers) for different text classification problems such as sentiment analysis or 20 news group classification using Tensorflow and Keras in Python
Text classification is one of the important and common tasks in supervised machine learning. It is about assigning a category (a class) to documents, articles, books, reviews, tweets or anything that involves text. It is a core task in natural language processing.
Many applications appeared to use text classification as the main task, examples include spam filtering, sentiment analysis, speech tagging, language detection, and many more.
In this tutorial, we will build a text classifier model using RNNs using Tensorflow in Python, we will be using IMDB reviews dataset which has 50K real world movie reviews along with their sentiment (positive or negative). In the end of this tutorial, I will show you how you can integrate your own dataset so you can train the model on it.
In this post, we'll learn top 30 Python Tips and Tricks for Beginners
You can learn how to use Lambda,Map,Filter function in python with Advance code examples. Please read this article
Keras vs Tensorflow - Learn the differences between Keras and Tensorflow on basis of Ease to use, Fast development,Functionality,flexibility,Performance etc
We will go over what is the difference between pytorch, tensorflow and keras in this video. Pytorch and Tensorflow are two most popular deep learning frameworks. Pytorch is by facebook and Tensorflow is by Google. Keras is not a full fledge deep learning framework, it is just a wrapper around Tensorflow that provides some convenient APIs.
Word Embedding using Keras Embedding Layer | Deep Learning Tutorial (Tensorflow, Keras & Python) | We will discuss how exactly word embeddings are computed. There are two techniques for this (1) supervised learning (2) self supervised learning techniques such as word2vec, glove. In this tutorial we will look at the first technique of supervised learning. We will also write code for food review classification and see how word embeddings are calculated while solving that problem