diff --git a/python_bindings/README.md b/python_bindings/README.md
index 2dcd145..4752886 100644
--- a/python_bindings/README.md
+++ b/python_bindings/README.md
@@ -47,11 +47,11 @@ neighbours = index.knnQueryBatch(data, k=10, num_threads=4)
 
 #### Logging
 
-NMSLIB produces quite a few informational messages. By default, they are not shown in Python. To enable debugging, one should use the following commands before importing the library:
+NMSLIB produces quite a few informational messages. By default, they are not shown in Python. To enable debugging, one should use the following commands **before** importing the library:
 
 ```
 import logging
-logging.basicConfig(level=logging.INFO)
+logging.basicConfig(level=logging.DEBUG)
 ```
 
 #### Installing with Extras
diff --git a/python_bindings/notebooks/README.md b/python_bindings/notebooks/README.md
index b97f414..593a789 100644
--- a/python_bindings/notebooks/README.md
+++ b/python_bindings/notebooks/README.md
@@ -1,4 +1,4 @@
-We have three Notebooks: two are for dense spaces and one is for the sparse space. For the dense space, we have examples of the so-called optimized and non-optimized indices. Except HNSW, all the methods save meta-indices rather than real onese. Meta indices contain only index structure, but not the data. Hence, before a meta-index can be loaded, we need to re-load data.
+We have three Notebooks: three are for dense spaces and one is for the sparse space. For the dense space, we have examples of the so-called optimized and non-optimized indices. Except HNSW, all the methods save meta-indices rather than real onese. Meta indices contain only index structure, but not the data. Hence, before a meta-index can be loaded, we need to re-load data. One example is a memory efficient space to search for SIFT vectors.
 
 HNSW, can save real indices, but only for the dense spaces: Euclidean and the cosine. When you use these optimized indices, the search does not require reloading all the data. However, reloading the data is **required** if you want to use the function **getDistance**. Furthermore, creation of the optimized index can always be disabled specifying the index-time parameter **skip_optimized_index** (value 1).