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Chapter 6
Lesson 170: Early Stopping
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... -
Chapter 6
Lesson 169: Bayesian Optimization
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... -
Chapter 6
Lesson 168: Random Search
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... -
Chapter 6
Lesson 167: Grid Search
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... -
Chapter 6
Lesson 166: The Importance of Hyperparameter Tuning
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... -
Chapter 6
Lesson 165: What Are Hyperparameters?
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... -
Chapter 6
Lesson 164: Details of Cross-Validation
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... -
Chapter 6
Lesson 163: Using Validation Sets
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... -
Chapter 6
Lesson 162: Analyzing Learning Curves
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... -
Chapter 6
Lesson 161: Coefficient of Determination (R²)
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... -
Chapter 4
Lesson 99: Speech Synthesis (Text-to-Speech)
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... -
Chapter 5
Lesson 147: Real-Time Data Processing
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... -
Chapter 5
Lesson 148: Utilizing Data Visualization Tools
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 ... -
Chapter 5
Lesson 149: Data Storytelling
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... -
Chapter 6
Lesson 156: F1 Score
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...
