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Recap and Today's Topic Hello! In the previous session, we learned about the basics of neural networks. Neural networks have a hierarchical structure consisting of input, hidden, and output layers, and they mimic the brain's neural circu...

Recap and This Week’s Topic Hello! In the previous lesson, we discussed CatBoost, a powerful boosting algorithm specialized for categorical variables. CatBoost offers automatic handling of categorical data and helps prevent overfitting, ...

Recap and Today's Topic Hello! Last time, we learned about LightGBM, a highly efficient gradient boosting framework known for its speed and ability to handle large datasets. Today, we will explore CatBoost, another boosting method that e...

Recap and Today's Topic Hello! Last time, we discussed XGBoost, a highly accurate and computationally efficient algorithm widely used in data science. Today, we’ll explore LightGBM (Light Gradient Boosting Machine), another gradient boos...

Recap and Today's Topic Hello! Last time, we learned about boosting, an ensemble learning technique that sequentially trains models and corrects errors to improve overall performance. Today, we will focus on XGBoost—an extremely high-per...

Recap and Today's Topic Hello! Last time, we explored ensemble learning, a technique that combines multiple models to improve accuracy. Ensemble learning enhances overall predictive power by compensating for the weaknesses of individual ...

Recap of the Previous Lesson and Today’s Topic Hello! In our previous session, we explored Bagging, an ensemble learning method that uses data resampling to train multiple models in parallel, combining their results to make stable predic...

Recap of the Previous Lesson and Today’s Topic Hello! In the last session, we explored Naive Bayes Classification, a simple and fast algorithm that is widely used in spam filtering and text classification. Today, we will discuss Ensemble...

Recap of the Previous Lesson and Today’s Topic Hello! In the last session, we explored k-Nearest Neighbors (k-NN), a simple yet effective algorithm that makes predictions based on the proximity of nearby data points. Today, we will cover...

Recap of the Previous Lesson and Today’s Topic Hello! In the previous session, we discussed Support Vector Machines (SVM), a powerful algorithm for classifying data by finding optimal boundaries between classes. Today, we will learn abou...

Recap of the Previous Lesson and Today’s Topic Hello! In the last session, we learned about Gradient Boosting, an ensemble learning technique that builds powerful models by gradually correcting errors. Today, we’ll dive into another powe...

Recap of the Previous Lesson and Today’s Topic Hello! In our previous session, we learned about Random Forest, an ensemble learning technique that combines multiple decision trees to compensate for the weaknesses of individual trees, cre...

Recap of the Previous Lesson and Today’s Topic Hello! In the last session, we learned about the decision tree algorithm. This simple yet powerful algorithm is widely used across various fields for tasks like classification and prediction...

How to Depict Fireworks Using AI Basics of AI Generators and Prompts When using AI to depict fireworks, the level of detail in the prompt greatly affects the result. By specifying the type of fireworks, such as star mines, rapid-fire seq...

How to Depict Park Landscapes Using AI Basics of AI Generators and Prompts When using AI to depict park landscapes, the level of detail in the prompt greatly influences the result. By specifying specific elements like urban parks, natura...