Learning AI from scratch– category –

Recap and Today's Theme Hello! In the previous episode, we introduced OpenCV and learned how to perform basic operations like loading, displaying, resizing, and color space conversion. Today, we will discuss the important topic of image ...

Recap and Today's Theme Hello! In the previous episode, we explained how to handle image data. We covered the basics of pixel data and learned how to load images using Python. In this episode, we will introduce OpenCV, a powerful library...

Recap and Today's Theme Hello! In the previous episode, we introduced the concept of Computer Vision and discussed how to extract information from images and videos. Today, we will learn about how to handle image data, focusing on the co...

Recap and Today's Theme Hello! In the previous episode, we reviewed Chapter 9 and conducted a knowledge check to deepen our understanding of Natural Language Processing (NLP). Now, we’re entering Chapter 10, where we will start learning ...

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 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 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 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 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 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...