モデルの評価とチューニング（151～180）– モデルの性能を正確に評価し、最適化するための手法を学びます。 –

Recap: Enhancing Model Interpretability In the previous episode, we explained how to interpret model predictions using SHAP values (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations). These techniqu...

Recap: Knowledge Distillation In the previous episode, we explained Knowledge Distillation, a technique that allows the transfer of knowledge from a large model to a smaller one. This method helps reduce model size while maintaining perf...

Recap: Model Optimization for Lightweight and Fast Inference In the previous article, we discussed techniques for optimizing models to enhance their inference speed and reduce their size. Specifically, we focused on methods such as model...

Recap: Improving Performance with Ensemble Learning In the previous lesson, we discussed Ensemble Learning, a method that combines multiple models to achieve higher accuracy than individual models alone. We introduced three major techniq...

Recap: Batch Normalization In the previous lesson, we discussed Batch Normalization, a technique that stabilizes the data distribution across layers in a neural network, improving learning stability and accelerating convergence. Batch No...

Recap: Dropout In the previous lesson, we detailed Dropout, a technique that deactivates some neurons randomly during training to prevent overfitting in neural networks. By ensuring that the model does not rely on specific neurons, Dropo...

Recap: Regularization In the previous lesson, we discussed the importance of Regularization techniques, such as L1 and L2 Regularization, which control model complexity and prevent overfitting. These methods help models avoid fitting the...

Recap: Learning Rate Scheduling In the previous lesson, we discussed Learning Rate Scheduling, a technique that dynamically adjusts the learning rate to facilitate efficient learning. By starting with a larger learning rate for quick ini...

Recap: Early Stopping In the previous lesson, we discussed Early Stopping, a technique to prevent overfitting by stopping training when validation error starts to increase. This method allows for improved generalization performance while...

Recap: Stacking In the previous lesson, we explored Stacking, an ensemble learning technique that combines different types of models using a meta model to achieve optimal predictions. This method allows for greater accuracy and improved ...

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...