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 Bag-of-Words (BoW) model, a simple method that represents text as vectors based on word frequency. While it’s effective for many NLP tasks, the BoW model has limita...

Recap and Today's Theme Hello! In the previous episode, we discussed TF-IDF, a method for evaluating word importance based on word frequency in documents and across a corpus. While TF-IDF is effective for many applications, it does not a...

Recap and Today's Theme Hello! In the previous episode, we explored Word2Vec, a model that learns the semantic similarity of words using two methods: CBOW and Skip-gram. Word2Vec captures meaning by examining the context surrounding each...

Recap and Today's Theme Hello! In the previous episode, we explored Word Embeddings, where words are represented as low-dimensional vectors that capture semantic relationships, improving NLP task performance. Compared to BoW and TF-IDF, ...

Recap and Today's Theme Hello! In the previous episode, we discussed morphological analysis in Japanese. Morphological analysis is an essential process for breaking down Japanese text into words and assigning part-of-speech information, ...

Recap and Today's Theme Hello! In the previous episode, we explored text data preprocessing, covering techniques such as tokenization, stopword removal, part-of-speech tagging, and n-gram creation. These methods are fundamental in transf...

Recap and Today's Theme Hello! In the previous episode, we learned about the basics and applications of Natural Language Processing (NLP). NLP technology processes text and speech data for various applications, including search engines, ...

Recap and Today's Theme Hello! In the previous episode, we explored version control with Git, covering the basics of code management and team collaboration. Using Git allows developers to manage project history, develop with multiple bra...

Recap and Today's Theme Hello! In the previous episode, we concluded Chapter 8 by reviewing the foundational steps in AI development, including building, evaluating, and tuning models. Today, we begin a new theme: Natural Language Proces...

Recap and Today's Theme Hello! In the previous episode, we explored environment setup with Docker, learning how to ensure reproducibility using container technology. By using Docker, we minimized differences in environments and enabled s...

Recap and Today's Theme Hello! In the previous episode, we discussed running models in cloud environments like AWS and GCP, which allowed us to build scalable and reliable systems that can handle large traffic volumes using cloud service...

Recap and Today's Theme Hello! In the previous episode, we explored creating and publishing APIs, using Flask to turn a trained model into an API and deploying it with Heroku. This enabled us to make prediction functions accessible from ...

Recap and Today's Theme Hello! In the previous episode, we learned how to use Flask to integrate a trained model into a web application, allowing users to input data via a form and view prediction results in real-time. Today, we will foc...

Recap and Today's Theme Hello! In the previous episode, we learned how to deploy models using Flask by building a simple Web API. We saw how an API can provide access to trained models, allowing real-time predictions as a service. This t...

Recap and Today's Theme Hello! In the previous episode, we explored transfer learning, learning how to adapt pre-trained models to new tasks. We saw how transfer learning allows for efficient construction of high-accuracy models even wit...