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Multi-Topic (Multi-Class) Text Classification Part E: A Deep Learning Model with Embedding Layer
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Related Tutorial Playlists in English:
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Diğer İlgili Eğitimler (Türkçe):
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Multi-Topic (Multi-Class) Text Classification Part E: A Model with
Keras Embedding Layer
The Sample Dataset: In this tutorial, I will use a Multi-Class Classification Dataset for Turkish. It is a benchmark dataset for the Turkish text classification task.
This is the Part B 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, 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. What is Text Classification? Text classification is a machine learning technique that assigns a set of predefined categories (labels/classes/topics) to open-ended text. The categories depend on the selected dataset and can cover arbitrary subjects. Therefore, text classifiers can be used to organize, structure, and categorize any kind of text.
Types of Classifications: In general, there are 3 types of classification Binary classification Multi-class classification
Multi-label classification
How and Where can we use Text Classifiers?
Text classifiers can be used to organize, structure, and categorize text.
Automatic Text Classification Approaches?
Related Tutorial Playlists in English:
-------------
Diğer İlgili Eğitimler (Türkçe):
--------------
Multi-Topic (Multi-Class) Text Classification Part E: A Model with
Keras Embedding Layer
The Sample Dataset: In this tutorial, I will use a Multi-Class Classification Dataset for Turkish. It is a benchmark dataset for the Turkish text classification task.
This is the Part B 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, 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. What is Text Classification? Text classification is a machine learning technique that assigns a set of predefined categories (labels/classes/topics) to open-ended text. The categories depend on the selected dataset and can cover arbitrary subjects. Therefore, text classifiers can be used to organize, structure, and categorize any kind of text.
Types of Classifications: In general, there are 3 types of classification Binary classification Multi-class classification
Multi-label classification
How and Where can we use Text Classifiers?
Text classifiers can be used to organize, structure, and categorize text.
Automatic Text Classification Approaches?
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