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Recap and Today's Theme Hello! In the previous episode, we built a CNN (Convolutional Neural Network) to classify handwritten digits. CNNs are powerful tools for image processing as they learn features hierarchically from image data. Thi...

Recap and Today's Theme Hello! In the previous episode, we discussed hyperparameter tuning to maximize model performance. By using grid search to find the best hyperparameters, we were able to significantly improve the model's accuracy. ...

Recap and Today's Theme Hello! In the previous episode, we discussed various methods for evaluating machine learning models, including accuracy, precision, recall, F1 score, ROC curves, and AUC scores. These metrics allow us to evaluate ...

Recap and Today's Theme Hello! In the previous episode, we used Python’s Keras library to build a basic neural network for classifying handwritten digits. Keras provides a simple API, making it an excellent tool for beginners to create m...

Recap and Today's Theme Hello! In the previous episode, we learned how to solve classification problems using a logistic regression model. We covered the fundamental steps, from data preprocessing to model training, prediction, and evalu...

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, ...