Part C: Prepare The Dataset

Multi-Topic Text Classification with Various Deep Learning Models

Author: Murat Karakaya
Date created….. 17 09 2021
Date published… 15 03 2022
Last modified…. 15 03 2022

Description: This is the Part C of the tutorial series “Multi-Topic Text Classification with Various Deep Learning Models” that covers all the phases of text classification:

• Exploratory Data Analysis (EDA),
• Text preprocessing
• TF Data Pipeline
• Keras TextVectorization preprocessing layer
• Multi-class (multi-topic) text classification
• Deep Learning model design & end-to-end model implementation
• Performance evaluation & metrics
• Generating classification report
• Hyper-parameter tuning
• etc.

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

We will use a Kaggle Dataset in which there are 32 topics and more than 400K total reviews.

If you would like to learn more about Deep Learning with practical coding examples,

• Please subscribe to the Murat Karakaya Akademi YouTube Channel or
• Do not forget to turn on notifications so that you will be notified when new parts are uploaded.
• Follow my blog on muratkarakaya.net

You can access all the codes, videos, and posts of this tutorial series from the links below.

        Photo by Jeffrey F Lin on Unsplash

 

Part D: Preprocessing Text with TF Data Pipeline and Keras Text Vectorization Layer

Multi-Topic Text Classification with Various Deep Learning Models

Author: Murat Karakaya
Date created….. 17 09 2021
Date published… 13 03 2022
Last modified…. 23 06 2022

Description: This is the Part D of the tutorial series “Multi-Topic Text Classification with Various Deep Learning Models” which covers all the phases of text classification:

• Exploratory Data Analysis (EDA),
• Text preprocessing
• TF Data Pipeline
• Keras TextVectorization preprocessing layer
• Multi-class (multi-topic) text classification
• Deep Learning model design & end-to-end model implementation
• Performance evaluation & metrics
• Generating classification report
• Hyper-parameter tuning
• etc.

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

We will use a Kaggle Dataset in which there are 32 topics and more than 400K total reviews.

If you would like to learn more about Deep Learning with practical coding examples,

• Please subscribe to the Murat Karakaya Akademi YouTube Channel or
• Do not forget to turn on notifications so that you will be notified when new parts are uploaded.
• Follow my blog at muratkarakaya.net

You can access all the codes, videos, and posts of this tutorial series from the links below.

• 
  

PARTS

In this tutorial series, there are several parts to cover the Text Classification with various Deep Learning Models topics. You can access all the parts from this index page.

Photo by 苏 静斋 on Unsplash

 

Part E: Text Classification with an Embedding Layer in a Feed-Forward Network

Multi-Topic Text Classification with Various Deep Learning Models

Author: Murat Karakaya
Date created….. 17 09 2021
Date published… 16 03 2022
Last modified…. 16 03 2022

Description: This is the Part E of the tutorial series “Multi-Topic Text Classification with Various Deep Learning Models” which covers all the phases of multi-class text classification:

• Exploratory Data Analysis (EDA),
• Text preprocessing
• TF Data Pipeline
• Keras TextVectorization preprocessing layer
• Multi-class (multi-topic) text classification
• Deep Learning model design & end-to-end model implementation
• Performance evaluation & metrics
• Generating classification report
• Hyper-parameter tuning
• etc.

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

We will use a Kaggle Dataset in which there are 32 topics and more than 400K total reviews.

If you would like to learn more about Deep Learning with practical coding examples,

• Please subscribe to the Murat Karakaya Akademi YouTube Channel or
• Do not forget to turn on notifications so that you will be notified when new parts are uploaded.
• Follow my blog on muratkarakaya.net

You can access all the codes, videos, and posts of this tutorial series from the links below.

• 
  

Photo by Alfred Schrock on Unsplash