diff --git a/ch04/README.rst b/ch04/README.rst
index 7fe0a92f..99a3c186 100644
--- a/ch04/README.rst
+++ b/ch04/README.rst
@@ -4,6 +4,16 @@ Chapter 4
 
 Support code for *Chapter 4: Topic Modeling*
 
+
+AP Data
+-------
+
+To download the AP data, use the ``download_ap.sh`` script inside the ``data``
+directory::
+
+    cd data
+    ./download_ap.sh
+
 Word cloud creation
 -------------------
 
diff --git a/ch04/blei_lda.py b/ch04/blei_lda.py
index bbad9d1f..7f6ac2b3 100644
--- a/ch04/blei_lda.py
+++ b/ch04/blei_lda.py
@@ -36,9 +36,9 @@
 # Iterate over all the topics in the model
 for ti in range(model.num_topics):
     words = model.show_topic(ti, 64)
-    tf = sum(f for f, w in words)
+    tf = sum(f for _, f in words)
     with open('topics.txt', 'w') as output:
-        output.write('\n'.join('{}:{}'.format(w, int(1000. * f / tf)) for f, w in words))
+        output.write('\n'.join('{}:{}'.format(w, int(1000. * f / tf)) for w, f in words))
         output.write("\n\n\n")
 
 # We first identify the most discussed topic, i.e., the one with the
diff --git a/ch04/data/download_ap.sh b/ch04/data/download_ap.sh
index 6de8ded8..da27814a 100755
--- a/ch04/data/download_ap.sh
+++ b/ch04/data/download_ap.sh
@@ -1,3 +1,3 @@
 #!/bin/sh
-wget http://www.cs.princeton.edu/~blei/lda-c/ap.tgz
+wget http://www.cs.columbia.edu/~blei/lda-c/ap.tgz
 tar xzf ap.tgz
diff --git a/ch04/wordcloud.py b/ch04/wordcloud.py
index 6c5302ea..accca2d6 100644
--- a/ch04/wordcloud.py
+++ b/ch04/wordcloud.py
@@ -24,8 +24,6 @@ def create_cloud(oname, words,maxsize=120, fontname='Lobster'):
     # gensim returns a weight between 0 and 1 for each word, while pytagcloud
     # expects an integer word count. So, we multiply by a large number and
     # round. For a visualization this is an adequate approximation.
-    # We also need to flip the order as gensim returns (value, word), whilst
-    # pytagcloud expects (word, value):
-    words = [(w,int(v*10000)) for v,w in words]
+    words = [(w,int(v*10000)) for w,v in words]
     tags = make_tags(words, maxsize=maxsize)
     create_tag_image(tags, oname, size=(1800, 1200), fontname=fontname)
diff --git a/ch12/image-classification.py b/ch12/image-classification.py
index 09dbd5b4..6f76d26d 100644
--- a/ch12/image-classification.py
+++ b/ch12/image-classification.py
@@ -39,7 +39,7 @@ def compute_texture(im):
 
 @TaskGenerator
 def chist(fname):
-    from features import color_histogram
+    from features import chist as color_histogram
     im = mh.imread(fname)
     return color_histogram(im)