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Recap and Today's Theme Hello! In the previous episode, we discussed evaluation methods for language models, focusing on metrics like Perplexity, BLEU, and ROUGE to measure performance. Today, we will explore N-gram models, a fundamental...

Recap and Today's Theme Hello! In the previous episode, we covered Seq2Seq models for translation, explaining how they transform sequences into other sequences using encoders and decoders, widely used in machine translation. Today, we wi...

Recap and Today's Theme Hello! In the previous episode, we explored the basics of dialogue systems, explaining how chatbots work and demonstrating both rule-based and AI-based implementations. Today, we will discuss Seq2Seq (Sequence-to-...

Recap and Today's Theme Hello! In the previous episode, we discussed Thesaurus and WordNet, resources that capture the semantic relationships between words and are widely used in NLP tasks. Today, we will explore the basics of Dialogue S...

Recap and Today's Theme Hello! In the previous episode, we discussed cosine similarity for text comparison, a technique used to measure the similarity between documents by converting them into vectors. Cosine similarity is widely applied...

Recap and Today's Theme Hello! In the previous episode, we covered Named Entity Recognition (NER), a technique for extracting and categorizing proper nouns within text. NER is widely applied in tasks like information extraction and impro...

Recap and Today's Theme Hello! In the previous episode, we explored fine-tuning BERT, explaining how to adapt a pre-trained model for specific NLP tasks. Today, we will delve into Topic Modeling (LDA). Topic modeling is a method for auto...

Recap and Today's Theme Hello! In the previous episode, we covered the basics of Topic Modeling (LDA), explaining how to extract latent topics from documents. LDA is a powerful technique for summarizing and organizing text data. Today, w...

Recap and Today's Theme Hello! In the previous episode, we discussed the implementation of the Transformer model, an innovative architecture that uses Self-Attention to enhance NLP performance. Today, we dive into fine-tuning BERT (Bidir...

Recap and Today's Theme Hello! In the previous episode, we explained the implementation of the Attention Mechanism, a technology that focuses on essential information within sequence data. Attention is especially effective in capturing l...

Recap and Today's Theme Hello! In the previous episode, we covered the basics of sentiment analysis, explaining how to determine emotions and opinions from text data using dictionary-based and machine learning-based methods. We also high...

Recap and Today's Theme Hello! In the previous episode, we discussed text classification using LSTM, a deep learning model designed to handle sequential data. LSTM excels at capturing long-term dependencies in sequences, allowing for acc...

Recap and Today's Theme Hello! In the previous episode, we implemented a news article classification model using Python. We covered the steps from data preprocessing to feature extraction, model construction, and evaluation. Document cla...

Recap and Today's Theme Hello! In the previous episode, we explored the basics of the deep learning framework TensorFlow. Using TensorFlow, we efficiently implemented various machine learning models, from tensor operations to building ne...

Recap and Today's Theme Hello! In the previous episode, we explored how to build models using Keras. We discovered how Keras makes it simple to construct anything from basic fully connected networks to complex custom networks. Today, we ...