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Recap: Bayesian Optimization In the previous lesson, we explored Bayesian Optimization, an efficient method that uses past trial results to guide the selection of promising hyperparameters. This approach allows for finding near-optimal s...

Recap: Random Search In the previous lesson, we covered Random Search, a method for hyperparameter optimization that selects a subset of combinations randomly instead of testing all combinations. This approach is efficient in terms of co...

Recap: Grid Search In the previous lesson, we explored Grid Search, a method that exhaustively tests all combinations of hyperparameters to find the optimal set. While effective, Grid Search can be computationally expensive and inefficie...

Recap: The Importance of Hyperparameter Tuning In the previous lesson, we discussed the importance of Hyperparameter Tuning. Hyperparameters are crucial settings that significantly affect a model’s performance. By setting the right value...

Recap: What Are Hyperparameters? In the previous lesson, we discussed Hyperparameters, the settings that significantly influence the learning process of a model. Examples include learning rate, batch size, number of epochs, and regulariz...

Recap: Details of Cross-Validation In the previous lesson, we discussed Cross-Validation, a technique used to accurately evaluate a model’s generalization performance. We explored various methods, including K-Fold Cross-Validation, which...

Recap: Using Validation Sets In the previous lesson, we discussed using Validation Sets to evaluate a model’s generalization performance. Validation sets play a critical role in adjusting models to prevent overfitting and selecting the b...

Recap: Analyzing Learning Curves In the previous lesson, we explored how to use Learning Curves to visually evaluate a model’s training process. Learning curves, which plot training and validation errors, help identify signs of overfitti...

Recap: Coefficient of Determination (R²) In the previous lesson, we covered the Coefficient of Determination (R²), a metric that measures how much of the variance in the data a regression model can explain. R² ranges from 0 to 1, with va...

Recap: Mean Absolute Error (MAE) In the previous lesson, we discussed Mean Absolute Error (MAE), a metric that calculates the average absolute difference between predicted and actual values. MAE is useful when the impact of outliers need...

Recap of the Previous Lesson: The Basics of Speech Recognition In the previous article, we covered speech recognition, a technology that analyzes speech data in real time and converts it into text. We explored how it is used in various f...

Recap: Network Data Analysis In the previous lesson, we explored network data analysis, learning how to use graph data composed of nodes and edges to visualize and analyze relationships across various domains, including social networks a...

Recap: Real-Time Data Processing In the previous lesson, we explored how to efficiently process streaming data using frameworks and time window techniques. This time, we will focus on data visualization tools and explain how to use them ...

Recap: Utilizing Data Visualization Tools In the previous lesson, we discussed popular data visualization libraries like Matplotlib, Seaborn, and Plotly. These tools help present data in a visual format, making patterns and trends easier...

Recap: Recall In the previous lesson, we discussed Recall, a metric that measures how well a model identifies actual positive instances within the dataset. Recall is crucial when minimizing False Negatives (FN) is vital, such as in medic...