replace x with t

Author: qualiaMachine

_episodes/05-tf-idf-documentEmbeddings.md

@@ -55,14 +55,14 @@ However, rare is a relative term. In a corpus of documents about blue whales, th
 
 One method for constructing more advanced word embeddings is a model called TF-IDF.
 
-TF-IDF stands for term frequency-inverse document frequency. The model consists of two parts: term frequency and inverse document frequency. We multiply the two terms to get the TF-IDF value.
+TF-IDF stands for term frequency-inverse document frequency and can be calculated for each document, *d*, and term, *t*, in a corpus. The calculation consists of two parts: term frequency and inverse document frequency. We multiply the two terms to get the TF-IDF value.
 
-**Term frequency(t,d)** is a measure how frequently a term, *t*, occurs in a document, *d*. The simplest way to calculate term frequency is by simply adding up the number of times a term occurs in a document, and dividing by the total word count in the document.
+**Term frequency(t,d)** is a measure for how frequently a term, *t*, occurs in a document, *d*. The simplest way to calculate term frequency is by simply adding up the number of times a term occurs in a document, and dividing by the total word count in the document.
 
 **Inverse document frequency** measures a term's importance. Document frequency is the number of documents, *N*, a term occurs in, so inverse document frequency gives higher scores to words that occur in fewer documents.
 This is represented by the equation:
 
-IDF(x) = ln[(N+1) / (DF(T)+1)]
+IDF(t) = ln[(N+1) / (DF(T)+1)]
 
 where...
 * N represents the total number of documents in the corpus