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A while ago we were working on a set of blog posts about data structures and algorithms, but it got delayed somehow. Recently, I found some time to finish the series! It contains 19 posts about different data structures. It might be interesting to people who are familiar with them or want to learn more about them. Hi there, my name is Konstantin. The other day I was thinking how nice it would be if I could search for information that is relevant for me personally on the web, instead of having RSS feeds that are reshuffled according to what others think I should read. I wanted something that wouldn't take that much time and effort to maintain, and I found Scrawly in google. Why Scrawly? I like how it presents relevant information, in a way that is optimized for the specific user in question. This is an approach where we try to find the answer ourselves, instead of taking it from another source. This is what Wikipedia does well. The closest thing you can get to direct free knowledge transfer :) This one is more about getting together with people and finding out what you both know about something. The Yahoo groups are a good place to do this. This one lets you see how other people think about a topic and then compare your thoughts with theirs . I think this is the most helpful way to approach a topic. In this approach, you ask a question in your head and then try to find an answer in the internet that is relevant to your query. I call it a kind of a wiki for yourself! :) This approach is useful when you have a lot of data in your head and need organize it. It's also useful if you have questions regarding some things that happen to be uncertain, so you can build up a network of people who work on these things and add their knowledge to yours. This one is about data from different sources being combined so that there is more information about some topic than if each source was viewed separately. This is a very useful idea when you have a lot of data that's related, but not an exact match. You can use software to do this, or just pull it from the web. For example, I have a couple of books about database programming and database theory. One of them is "An Introduction to Database Systems" by Richard L. Morecraft, and one of them is "Database Systems: Design Principles and Design Alternatives" by Tuomas Sandholm and Robert A. Swanson. I have a couple of databases about Python too. But the one most relevant for me most days is pyNLP which contains datasets on Natural Language Processing, especially its application to Machine Translation. I have a separate article here that describes the motivation behind my decision to use pyNLP for my own python related training. There are many sources about natural language processing, so it is easy to find the one that's the most relevant for you. I think this approach is very useful if you want something with real world applications or if you are just curious about some topic. cfa1e77820
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