Learning AI from scratch– category –

Recap and Today's Theme Hello! In the previous episode, we explored the basics of data preprocessing, focusing on handling missing values and feature scaling. Proper preprocessing is essential as it prepares the data to be more suitable ...

Recap and Today's Theme Hello! In the previous episode, we implemented a linear regression model, one of the most fundamental models in machine learning, to predict trends based on data. Linear regression is a simple yet powerful method ...

Recap and Today's Theme Hello! In the previous episode, we learned about FastText, a word embedding method that uses subword information to handle unknown words and morphological variations, making it particularly useful for tasks like d...

Recap and Today's Theme Hello! In the previous episode, we covered the GloVe model, which captures the semantic relationships between words using a co-occurrence matrix. However, GloVe has limitations in dealing with out-of-vocabulary wo...

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 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 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 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 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 environment setup with Docker, learning how to ensure reproducibility using container technology. By using Docker, we minimized differences in environments and enabled s...