@@ -337,7 +337,7 @@ Developers can compare with multiple experiments by specifying and uploading the
**ROC Curve** shows the performance of a classification model at all classification thresholds; the larger the area under the curve, the better the model performs, aiding developers to evaluate the model performance and choose an appropriate threshold.
| [Graph](#Graph--Network-Structure) | Network Structure | Visualize network structures, node attributes and data flow, assisting developers to learn and to optimize network structures. |
| [Histogram](#Histogram--Distribution-of-Tensors) | Distribution of Tensors | Present the changes of distributions of tensors, such as weights/gradients/bias, during the training process. |
| [PR Curve](#PR-Curve) | Precision & Recall Curve | Display precision-recall curves across training steps, clarifying the tradeoff between precision and recall when comparing models. |
| [ROC Curve](#ROC-Curve) | Receiver Operating Characteristic curve | Shows the performance of a classification model at all classification thresholds |
| [ROC Curve](#ROC-Curve) | Receiver Operating Characteristic curve | Shows the performance of a classification model at all classification thresholds. |
| [High Dimensional](#High-Dimensional--Data-Dimensionality-Reduction) | Data Dimensionality Reduction | Project high-dimensional data into 2D/3D space for embedding visualization, making it convenient to observe the correlation between data. |
At the same time, VisualDL provides [VDL.service](#vdlservice) , which allows developers to easily save, track and share visualization results of experiments with anyone for free.
