Computing

[From the last episode: We looked at the reasons for all of the multiply-accumulate hardware in machine-learning engines.] OK, we’ve spent a couple of weeks deep in the math used for machine learning (ML). Now let’s back up a second to look at what this all means in the bigger […]

[From the last episode: We looked at the convolution that defines the CNNs that are so popular for machine vision applications.] This week we’re going to do some more math, although, in this case, it won’t be as obscure and bizarre as convolution – and yet we will find some […]

[From the last episode: We looked at CNNs for vision as well as other neural networks for other applications.] We’re going to take a quick detour into math today. For those of you that have done advanced math, this may be a review, or it might even seem to be […]

[From the last episode: We looked at the common machine-vision application and its primary .] We’ve seen that vision is a common AI these days, and we’ve also talked about the fact that our spate of neural networks are not neuromorphic – that is, they’re not modeled after animal brains. […]

[From the last episode: We looked at the difference between training and inference for ANNs.] One can do many, many things with AI. But of all of the applications, vision has been the most… well, visible. Another popular application – so-called predictive analytics – is used by the social media […]