correct the path of the images

Author: fbkarsdorp

answerbook/Chapter 10 - Learning without Supervision.ipynb

@@ -1,7 +1,7 @@
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@@ -34,7 +34,7 @@
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-      "<img src=\"images/cluster.png\" alt=\"cluster\" style=\"width: 280px;\" align=\"right\"/>\n",
+      "<img src=\"../images/cluster.png\" alt=\"cluster\" style=\"width: 280px;\" align=\"right\"/>\n",
       "In the previous chapter we have developed a system that on the basis of examples attempts to learn a function to classify new, unseen examples. Not always do we have the luxury of a labeled data set. In fact, most of the time only unlabeled data is available. In unsupervized machine learning, or learning without supervision, we attempt the create systems that detect patterns in our data, such as groupings or clusters. Given a collection of texts, we could for example try to measure the pairwise distances between all texts and given these distances construct a grouping of the texts. Another example of unsupervized learning is the popular method of *Topic Modeling* in which we attempt to find clusters of semantically coherent words that together form a topic. \n",
       "\n",
       "In this chapter we will introduce you to some of the techniques to cluster you data without supervision. As is the case with supervized learning, there are many different approaches to clustering. We will discuss one of the most popular ones: hierachical agglomerative clustering. We will develop a general hierarchical cluster module and implement a number of different cluster procedures. "

answerbook/Chapter 8 - PDF Search App.ipynb

@@ -1,7 +1,7 @@
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@@ -36,11 +36,11 @@
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       "Our Information Retrieval system of the previous chapter is simple and also quite efficient, but it lacks many features of a full-blown search engine. One particular downside of the system is that it stores the index in RAM memory. This means that either we have to keep it there or we have to rebuild the index each time we would like to search through a particular collection. We could try to improve our system, but there are some excellent Python packages for Information Retrieval and search engines, we could use. In this section we will explore one of them, called [Whoosh](http://whoosh.readthedocs.org/en/latest/), a search engine and retrieval system written in pure Python. \n",
       "\n",
-      "![whoosh](files/images/whoosh_logo.png)\n",
+      "![whoosh](../files/images/whoosh_logo.png)\n",
       "\n",
       "Ever since science-journal giant Elsevier bought the once so promising bibliography management software [Mendeley](http://www.mendeley.com/), I have looked for alternative ways to manage my research PDF collection. For me, one of the most important features of a PDF management tool is to be able to do full text search in the PDFs for the content I am interested in. Uptill today I have not found a tool that fulfills all my needs, so we are going to build one ourselves. We will develop a full-blown search using Whoosh. We'll build a web interface on top of [Flask](http://flask.pocoo.org/) to query our search engine in a user-friendly way. Just to tease you a bit: this is what our search engine will look like:\n",
       "\n",
-      "![pydf](files/images/pydf.png)\n",
+      "![pydf](../files/images/pydf.png)\n",
       "\n",
       "This chapter is the first in a series of more practical chapters, in which we will build actual applications ready for use by end-users. You won't be learning many new programming techniques in this chapter, but we will introduce you to a large number of modules and packages that are available either in the standard library of Python or as third-party packages. The most important take-home message is that if you think about implementing a piece of software, the first thing you should do, is check whether someone else hasn't done it before you. Chances are good that someone has, and she or he probably has done a better job. \n",
       "\n",