Tuesday, November 1, 2022

Multi-Topic (Multi-Class) Text Classification With Various Deep Learning Models Tutorial Series

Multi-Topic (Multi-Class) Text Classification With Various Deep Learning Models Tutorial Series

Index Page

This is the index page of the “Multi-Topic (Multi-Class) Text Classification With Various Deep Learning Models” tutorial series.

Author: Murat Karakaya
Date created….. 17 Sept 2021
Date published… 11 March 2022
Last modified…. 09 April 2023

Description: This is a tutorial series that covers all the phases of text classification: Exploratory Data Analysis (EDA) of text, text preprocessing, and multi-class (multi-topic) text classification using the TF Data Pipeline and the Keras TextVectorization preprocessing layer.

We will design various Deep Learning models by using the Keras Embedding layer, Convolutional (Conv1D) layer, Recurrent (LSTM) layer, Transformer Encoder block, and pre-trained transformer (BERT).

We will use a Kaggle Dataset with 32 topics and more than 400K reviews.

We will cover all the topics related to solving Multi-Class Text Classification problems with sample implementations in Python TensorFlow Keras.

You can access the codesvideos, and posts from the below links.

If you would like to learn more about Deep Learning with practical coding examples, please subscribe to the Murat Karakaya Akademi YouTube Channel or follow my blog on muratkarakaya.net. Remember to turn on notifications so that you will be notified when new parts are uploaded.

Photo by Patrick Tomasso on Unsplash


In this tutorial series, there will be several parts to cover the “Text Classification with various Deep Learning Models” in detail as follows. 

You can access all these parts on YouTube in ENGLISH or TURKISH!

You can access the complete codes as Colab Notebooks using the links given in each video description (Eng/TR) or you can visit the Murat Karakaya Akademi Github Repo.

Comments or Questions?

Please share your Comments or Questions.

Thank you in advance.

Do not forget to check out the following parts!

Take care!

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