December 2019
Intermediate to advanced
468 pages
14h 28m
English
Content preview from Advanced Deep Learning with Python
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Language Modeling
This chapter is the first of several in which we'll discuss different neural network algorithms in the context of natural language processing (NLP). NLP teaches computers to process and analyze natural language data in order to perform tasks such as machine translation, sentiment analysis, natural language generation, and so on. But to successfully solve such complex problems, we have to represent the natural language in a way that the computer can understand, and this is not a trivial task.
To understand why, let's go back to image recognition. The neural network input is fairly intuitive—a 2D tensor with preprocessed pixel intensities, which preserves the spatial features of the image. Let's take a 28 x 28 MNIST image, ...
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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.