May 2020
Intermediate to advanced
404 pages
10h 52m
English
Content preview from Hands-On Python Deep Learning for the Web
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Data retrieval
As mentioned earlier in this chapter, ML systems need data for functioning. It is not available all of the time, in fact, most of the time, the data itself is not available in a format with which we can actually start training ML models. But what if there is no standard dataset for a particular problem that we are trying to solve using ML? Welcome to reality! This happens for most real-life ML projects. For example, let's say we are trying to analyze the sentiments of tweets regarding the New Year resolutions of 2018 and trying to estimate the most meaningful ones. This is actually a problem for which there is no standard dataset available. We will have to scrape it from Twitter using its APIs. Another great example is business ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.