January 2018
Beginner to intermediate
284 pages
8h 35m
English
Content preview from Deep Learning Essentials
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Representation
Representation refers to the computer-interpretable description of the multimodal data (for example, vector and tensor). It covers the following, but is not limited to:
- How to handle different symbols and signals—for example, in machine translation, Chinese characters and English characters are two distinct linguistic systems; in a self-driving system, point clouds from LIDAR sensors and image pixels from the RGB camera are two distinct sources with distinct characteristics
- How to handle different granularities
- Modality can be either static or sequential
- Different noise distribution
- Unbalanced proportions.
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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.