生成AIの技術詳細（181～210）– 生成モデルの内部構造と技術を深く理解します。 –

Recap and Today's Theme Hello! In the previous episode, we discussed the challenges unique to Japanese NLP, such as structural features, tokenization difficulties, and the polysemy of Kanji. Today, we will explore the latest trends in NL...

Recap and Today's Theme Hello! In the previous episode, we discussed the challenges and limitations of NLP, such as ambiguity, context understanding difficulties, and the lack of world knowledge in models. Today, we will focus on the uni...

Recap and Today's Theme Hello! In the previous episode, we explored practical text generation using GPT-2 and other large language models, covering implementation and applications. Today, we will discuss the challenges and limitations of...

Recap and Today's Theme Hello! In the previous episode, we explained spell correction, detailing how to automatically fix typographical errors using methods like edit distance and language models. Today, we will delve into practical text...

Recap and Today's Theme Hello! In the previous episode, we explained N-gram models, which predict the next word based on the sequence of previous words. N-gram models are simple yet powerful for tasks like text generation and spell corre...

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 discussed the basics of text summarization, exploring both extractive and abstractive methods to efficiently grasp the key points of long texts such as news articles and reports....

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 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 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 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...