Digests » 86
uppose your friend just baked and shared an excellent cake with you, and you would like to know its recipe. It might seem that it should be very easy for your friend to just tell you how to cook the cake — that it should be easy for him to get across the recipe. But this is a subtler task than you might think; how detailed should the instructions be? Does the friend have to explain in detail each of the tiny tasks to be followed?
X-rays are a well-established tool to help analyze and restore valuable paintings because the rays' higher frequency means they pass right through paintings without harming them. X-ray imaging can reveal anything that has been painted over a canvas or where the artist may have altered his (or her) original vision. But the technique has its limitations, and that's where machine learning can prove useful. Two papers this fall illustrated the use of AI to solve specific problems in art analysis and conservation: one to reconstruct an underpainting in greater detail, and the other to make it easier to image two-sided painted panels.
In this post, we'll go through the necessary steps to build and deploy a machine learning application. This starts from data collection to deployment and the journey, as you'll see it, is exciting and fun 😀.
In this article, we'll study whether the daily realized volatility, RVt, of a stock index is better modelled by augmenting the heterogeneous autoregressive (HAR) model with Google search volumes for the name of the index, SVt.
Document classification is one of the common use cases in the domain of Natural Language Processing (NLP) and well applied in many applications. This example demonstrate document classification with the use case of spam mail filtering. The results shows that by using Deep Learning, we can strategically filter out most of the spam mails based on the context.