CMSIS-DSP: Improved doxygen and README

.gitignore

@@ -11,3 +11,6 @@ Examples/ARM/arm_linear_interp_example/RTE/
 Examples/ARM/arm_signal_converge_example/RTE/
 Examples/ARM/arm_svm_example/RTE/
 Projects/ARM/IntermediateFiles/
+.ipynb_checkpoints
+Examples/ARM/*/RTE/
+Examples/ARM/*/MTICoverageOut.cov

Examples/ARM/arm_bayes_example/arm_bayes_example_f32.c

@@ -48,7 +48,8 @@
  * \par Description:
  * \par
  * Demonstrates the use of Bayesian classifier functions. It is complementing the tutorial
- * about classical ML with CMSIS-DSP and python scikit-learn.
+ * about classical ML with CMSIS-DSP and python scikit-learn:
+ * https://developer.arm.com/solutions/machine-learning-on-arm/developer-material/how-to-guides/implement-classical-ml-with-arm-cmsis-dsp-libraries
  *
  */
 

Examples/ARM/arm_svm_example/arm_svm_example_f32.c

@@ -48,7 +48,8 @@
  * \par Description:
  * \par
  * Demonstrates the use of SVM functions. It is complementing the tutorial
- * about classical ML with CMSIS-DSP and python scikit-learn.
+ * about classical ML with CMSIS-DSP and python scikit-learn:
+ * https://developer.arm.com/solutions/machine-learning-on-arm/developer-material/how-to-guides/implement-classical-ml-with-arm-cmsis-dsp-libraries
  *
  */
 

PythonWrapper/README.md

@@ -6,6 +6,9 @@ It is a very experimental wrapper with lots of limitations as described in the c
 
 But even with those limitations, it can be very useful to test a CMSIS-DSP implemention of an algorithm with all the power of numpy and scipy.
 
+A tutorial is also available but with less details than this README:
+https://developer.arm.com/documentation/102463/latest/
+
 # How to build and install
 
 ## Tested configurations