HTML – Layouts

HTML – Layouts

 

HTML – Standard layout

A standard layout is composed of a banner in the upper part of the page, a menu in the left part, and the content zone in the middle or in the right. This is the most classic layout for a web site, and, in my opinion, a representative model.

A layout does not have many options. On the other side, a table is very useful. In a table you can add almost every element, even another table.

HTML Code:

<table cellspacing="1" cellpadding="0" border="0" bgcolor="black" id="shell" height="250" width="400">
	<tr height="50">
		<td colspan="2" bgcolor="white"> 
		
			<table title="Banner" id="banner" border="0"> 
				<tr>
					<td>Aici poti sa pui un baner, logo, etc</td>
				</tr> 
			</table>
		
		</td>
	</tr>
	<tr height="200">
		<td bgcolor="white"> 
		
			<table id="navigation" title="Navigation" border="0">
				<tr>
					<td>Link-uri!</td>
				</tr>
				<tr>
					<td>Link-uri!</td>
				</tr>
				<tr>
					<td>Link-uri!</td>
				</tr>
			</table> 
			
		</td>
		<td bgcolor="white">
			
			<table title="Content" id="content" border="0">
				<tr>
					<td>Continutul sitului va fi plasat aici</td>
				</tr> 
			</table> 
			
		</td>
	</tr>
</table>

 

This is a basic approach. As you will see if you use these layouts in your web page, it will become very complex as you add more content. Nevertheless, it is very important that you specify the height and the width. The more you will be more specific in establishing these attributes and not only these, the less bugs you will have.

Study this code a little bit, organize it so that you will be able to understand it. Copy it in a new document to see it.

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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