Showing posts with label Multi-Class Text Classification. Show all posts
Showing posts with label Multi-Class Text Classification. 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:

Saturday, November 19, 2022

Part F: Text Classification with a Convolutional (Conv1D) Layer in a Feed-Forward Network

 

Part F: Text Classification with a Convolutional (Conv1D) Layer in a Feed-Forward Network



Author: Murat Karakaya
Date created….. 17 09 2021
Date published… 11 03 2022
Last modified…. 29 12 2022

Description: This is the Part F 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.



Photo by Josh Eckstein on Unsplash

Tuesday, November 8, 2022

How to solve Multi-Class Classification Problems in Deep Learning with Tensorflow & Keras?

 

How to solve Multi-Class Classification Problems in Deep Learning with Tensorflow & Keras?

This is the third part of the “How to solve Classification Problems in Keras?” series. If you have not gone over Part A and Part Bplease review them before continuing with this tutorial. The link to all parts is provided below.

In this tutorial, we will focus on how to solve Multi-Class Classification Problems in Deep Learning with Tensorflow & KerasFirst, we will download the MNIST dataset. In multi-class classification problems, we have two options to encode the true labels by using either:

  • integer numbers, or
  • one-hot vector

We will experiment with both encodings to observe the effect of the combinations of various last-layer activation functions and loss functions on a Keras CNN model’s performance. In both experiments, we will discuss the relationship between Activation & Loss functionslabel encodings, and accuracy metrics in detail.

We will understand why sometimes we could get surprising results when using different parameter settings other than the generally recommended ones. As a result, we will gain insight into activation and loss functions and their interactions. In the end, we will summarize the experiment results in a cheat table.

You can access the code at Colab and all the posts of the classification tutorial series at muratkarakaya.netYou can watch all these parts on YouTube in ENGLISH or TURKISH as well.

If you would like to follow up on Deep Learning tutorials, please subscribe to my 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.

If you are ready, let’s get started!



Photo by Debby Hudson on Unsplash


Friday, November 4, 2022

Classification in Deep Learning with TensorFlow & Keras Tutorial Series

 

Classification in Deep Learning with TensorFlow & Keras Tutorial Series

This is the index page of the “How to solve Classification Problems in Deep Learning with TensorFlow & Keras?” tutorial series.

We will cover all the topics related to solving 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 follow up on Deep Learning tutorials, please subscribe to my 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.

Last updated: 26/07/2022



Photo by Cristina Gottardi on Unsplash

Multi-Class Text Classification with a GPT3 Transformer block: An End-to-End Example

 

Multi-Class Text Classification with a GPT3 Transformer block: An End-to-End Example

Author: Murat Karakaya & Cansen Çağlayan
Date created: 05 Oct 2021
Last modified: 19 Oct 2021
Description: This tutorial has 2 parts as explained below. Part A: Data Analysis & Text Preprocessing and Part B: Text Classification. 



                                       Photo by Håkon Grimstad on Unsplash

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