diff --git a/docs/advanced-hyperopt.md b/docs/advanced-hyperopt.md
index 97d6ed9ba..5b7092f51 100644
--- a/docs/advanced-hyperopt.md
+++ b/docs/advanced-hyperopt.md
@@ -175,7 +175,6 @@ Possible values are either one of "NSGAIISampler", "TPESampler", "GPSampler", "C
 
 Some research will be necessary to find additional Samplers (from optunahub) for example.
 
-
 !!! Note
     While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
     If you're unsure about this, best use one of the Defaults (`"NSGAIIISampler"` has proven to be the most versatile) without further parameters.
diff --git a/docs/faq.md b/docs/faq.md
index 8d19b5db0..e0be88d14 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -219,10 +219,7 @@ On Windows, the `--logfile` option is also supported by Freqtrade and you can us
 First of all, most indicator libraries don't have GPU support - as such, there would be little benefit for indicator calculations.
 The GPU improvements would only apply to pandas-native calculations - or ones written by yourself.
 
-For hyperopt, freqtrade is using scikit-optimize, which is built on top of scikit-learn.
-Their statement about GPU support is [pretty clear](https://scikit-learn.org/stable/faq.html#will-you-add-gpu-support).
-
-GPU's also are only good at crunching numbers (floating point operations).
+GPU's are only good at crunching numbers (floating point operations).
 For hyperopt, we need both number-crunching (find next parameters) and running python code (running backtesting).
 As such, GPU's are not too well suited for most parts of hyperopt.