Showing posts with label Embedding Layer. Show all posts
Showing posts with label Embedding Layer. Show all posts

Saturday, April 8, 2023

Part G: Text Classification with a Recurrent Layer

 

Part G: Text Classification with a Recurrent Layer


Author: Murat Karakaya
Date created….. 17 02 2023
Date published… 08 04 2023
Last modified…. 08 04 2023

Description: This is the Part G 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),

We will design various Deep Learning models by using

  • Keras Embedding layer,

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,

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

Accessible on:


PARTS

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

In this part, we will use the Keras Bidirectional LSTM layer in a Feed Forward Network (FFN).

If you are not familiar with the Keras LSTM layer or the Recurrent Networks concept, you can check in the following Murat Karakaya Akademi YouTube playlists:

English:

Turkish

If you are not familiar with the classification with Deep Learning topic, you can find the 5-part tutorials in the below Murat Karakaya Akademi YouTube playlists:

Friday, November 4, 2022

Keras Text Vectorization Layer: Configure, Adapt, Use, Save, Load, and Deploy

 

Keras Text Vectorization Layer: Configure, Adapt, Use, Save, Load, and Deploy

Author: Murat Karakaya
Date created: 05 Oct 2021
Last modified: 18 March 2023
Description: This is a tutorial about how to build, adapt, use, save, load, and deploy the Keras TextVectorization layer. You can access this tutorial on YouTube in English and Turkish. TensorFlow Keras Text Vectorization Katmanı” / “TensorFlow Keras Text Vectorization Layer”. 

In this tutorial, we will download a Kaggle Dataset in which there are 32 topics and more than 400K total reviews. We will use this dataset for a multi-class text classification task.

Our main aim is to learn how to effectively use the Keras TextVectorization layer in Text Processing and Text Classification.

The tutorial has 5 parts:

  • PART A: BACKGROUND
  • PART B: KNOW THE DATA
  • PART C: USE KERAS TEXT VECTORIZATION LAYER
  • PART D: BUILD AN END-TO-END MODEL
  • PART E: DEPLOY END-TO-END MODEL TO HUGGINGFACE SPACES USING GRADIO
  • SUMMARY

At the end of this tutorial, we will cover:

  • What a Keras TextVectorization layer is
  • Why we need to use a Keras TextVectorization layer in Natural Language Processing (NLP) tasks
  • How to employ a Keras TextVectorization layer in Text Preprocessing
  • How to integrate a Keras TextVectorization layer to a trained model
  • How to save and load a Keras TextVectorization layer and a model with a Keras TextVectorization layer
  • How to integrate a Keras TextVectorization layer with TensorFlow Data Pipeline API (tf.data)
  • How to design, train, save, and load an End-to-End model using Keras TextVectorization layer
  • How to deploy the End-to-End model with a Keras  TextVectorization  layer implemented with a custom standardize (custom_standardization) function using the Gradio library and the HuggingFace Spaces

Accessible on:





Photo by Francois Olwage on Unsplash

Tuesday, November 1, 2022

Part A: A Practical Introduction to Text Classification

 

Part A: A Practical Introduction to Text Classification

Multi-Topic Text Classification with Various Deep Learning Models

Author: Murat Karakaya
Date created….. 17 09 2021
Date published… 11 03 2022
Last modified…. 12 03 2022

Description: This is the Part A of the tutorial series 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
  • Follow my blog on muratkarakaya.net
  • Do not forget to turn on notifications so that you will be notified when new parts are uploaded.


PARTS

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



Photo by Luisa Brimble on Unsplash

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

 

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