Multi-Topic (Multi-Class) Text Classification Part D: Keras TextVectorization layer TF Data Pipeline

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Related Tutorial Playlists in English:
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Diğer İlgili Eğitimler (Türkçe):
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TURKISH (ENGLISH BELOW)
Farklı Derin öğrenme modelleri ile Çok Sınıflı Metin Sınıflandırma: Bu, metin sınıflandırmanın tüm aşamalarını kapsayan eğitim serisinin dördüncü bölümüdür: Keşifsel Veri Analizi (EDA), Türkçe Metin Sınıflandırma, Metin ön işleme TF Veri Hattı, Keras TextVectorization ön işleme katmanı
Çok sınıflı metin sınıflandırması, Derin Öğrenme modeli tasarımı ve uçtan uca model uygulaması, Performans değerlendirmesi ve metrikler, Sınıflandırma raporu oluşturuluyor, Hiper parametre ayarı vb. kullanarak çeşitli Derin Öğrenme modelleri tasarlayacağız.
Keras Gömme katmanı, Evrişimsel (Conv1D) katman, Tekrarlayan (LSTM) katman, Transformer Encoder bloğu ve önceden eğitilmiş transformatör (BERT).
Python / TensorFlow / Keras ortamında örnek uygulamalarla Çok Sınıflı Metin Sınıflandırma yöntemleri. Metin Sınıflandırması Nedir?
Metin sınıflandırma, bir dizi önceden tanımlanmış kategoriyi (etiketler/sınıflar/konular) açık uçlu metne atayan bir makine öğrenimi tekniğidir. Sınıflandırma Türleri:
Genel olarak 3 tip sınıflandırma vardır: ikili sınıflandırma, Çok sınıflı sınıflandırma, Çok etiketli sınıflandırma.
Otomatik Metin Sınıflandırma Yaklaşımları Nelerdir? Otomatik metin sınıflandırmaya yönelik aşağıdakiler gibi birçok yaklaşım vardır: Kural tabanlı sistemler, Makine öğrenimi tabanlı sistemler, Derin öğrenme tabanlı sistemler, Hibrit sistemler

ENGLISH:
Multi-Topic (Multi-Class) Text Classification with Various Deep Learning Models Keras TextVectorization & TF Data Pipeline
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 D 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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Can you tell how to prepare the text for multi label classification

vikramsreeram

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