Advanced Tables and Queries   «Prev  Next»
Lesson 1

Introduction to Advanced, Tables, and Queries using Microsoft Access

Course introduction
Welcome to Advanced Tables, Queries, Forms, and Reports, the third course in the Microsoft Access® 2000 Series.
This course is the third part in a four-course series that gives you a hands-on introduction to the basic through advanced features of Microsoft Access 2000, Microsoft’s popular database program.
Microsoft Access is a very powerful database system, giving you features like no other program can. As with other systems, with power comes complexity. You can quickly get Access up and running using wizards. But once you have gone beyond where the wizards can take you, making Access perform more advanced tasks can become troublesome.
If you have been using Access for a while but feel you should be able to get more out of your database, this is the course for you.

Course objectives
After completing the course, you will be able to use Access to:
  1. Set up validation for fields
  2. Create custom validation messages
  3. Work with input masks
  4. Create and modify queries and forms
  5. Work with reports and subreports
  6. Add charts and graphics to forms and reports
  7. Work with the various kinds of controls, including ActiveX

Use the back propagation algorithm to learn a feature representation of the meaning of the word, I am going to start with a very simple case from the 1980s, when computers were very slow. It's a small case, but it illustrates the idea about how you can take some relational information, and use the back propagation algorithm to turn relational information into feature vectors that capture the meanings of words.
This diagram shows a simple family tree, in which, for example, Christopher and Penelope have children Arthur and Victoria.
What we would like is to train a neural network to understand the information in this family tree. We have also given it another family tree of Italian people which has pretty much the same structure as the English tree. Perhaps when it tries to learn both sets of facts, the neural net is going to be able to take advantage of that analogy.