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We've learned about RNNs, how they work, why they have become a big deal, we've trained an RNN character-level language model on several fun datasets, and we've seen where RNNs are going. You can confidently expect a large amount of innovation in the space of RNNs, and I believe they will become a pervasive and critical component to intelligent systems.
It's not easy to visualize a quantity that varies over time and which is composed of more than two subsegments. Take, for example, this stacked bar chart of religious affiliation of the Australian population, by time: While it's easy to see the how the share of Anglicans (at the bottom of the chart) has changed over time, it's much more difficult to assess the change in the "No religion" category: the separated bars coupled with the (necessarily) uneven positioning makes it hard to judge changes from year to year.
Introduction In our previous tutorial Loops in R: Usage and Alternatives , we discussed one of the most important constructs in programming: the loop. Eventually we deprecated the usage of loops in R in favor of vectorized functions. In this post we highlight some of the most used vectorized functions: the apply functions.
The for-loop in R, can be very slow in its raw un-optimised form, especially when dealing with larger data sets. There are a number of ways you can make your logics run fast, but you will be really surprised how fast you can actually go. This chapter shows a number of approaches including simple
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