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Series
In this series we will learn about Natural Language Processing (from Zero to Hero)
let's dive into the wonderful world of Recurrent Neural Networks (RNNs) — from the ground up to becoming a hero with a deep understanding of how they work, the challenges they face, and how to use them in real-world applications, especially in natura...
☟ Here are some good articles on this topics ☛ Long Short Term Memory - 1 ☛ Long Short Term Memory -2 ☛ Gated Recurrent Unit (GRU) ☛ CODE : LSTM Notebook
✨ “How can machines read language?” That's the fundamental question behind tokenization, the process of breaking down raw text into manageable, machine-readable pieces. If you're on your journey to becoming a data scientist or working with Natural La...
In the vast realm of Natural Language Processing (NLP), data preprocessing is the bedrock of all downstream tasks—whether it's sentiment analysis, machine translation, or text summarization. Before any model shines, the text must be cleaned, normaliz...
📌 What is Text Vectorization? In NLP, machines can’t understand text directly — they understand numbers. Text vectorization is the process of converting textual data into numerical vectors so that we can feed them into machine learning or deep learn...
