diff --git a/Adaptive-R-Peak-Detection-on-Wearable-ECG-Sensors-for-High-Intensity-Exercise.md b/Adaptive-R-Peak-Detection-on-Wearable-ECG-Sensors-for-High-Intensity-Exercise.md new file mode 100644 index 0000000..6fccdab --- /dev/null +++ b/Adaptive-R-Peak-Detection-on-Wearable-ECG-Sensors-for-High-Intensity-Exercise.md @@ -0,0 +1 @@ +
Fascinating stuff. Makes me feel like I really want to enhance my exercise routine. LLMs provide a bonus by eliminating the need for take a look at case development compared to traditional e-assessment systems. AI explainability is especially challenging when based on deep learning fashions, given that among the paths that AI programs use to provide recommendations are usually not interpretable Ehsan and Riedl (2020), and the supply of many generative outputs is advanced (e.g. Kovaleva et al, 2019). While understanding ML in its technical sense is vital, recent approaches in the explainability of AI have pointed at other ways of understandings which are not based on technical explanations and as an alternative, promote experimentation, difficult boundaries, or promoting respect Nicenboim et al (2022) \ No newline at end of file