### Netezza on Spark

In this blog we are gonna see how to use Spark to read tables from Netezza.

In this blog we are gonna see how to use Spark to read tables from Netezza.

Document image classification is not as well studied as natural image classification. We experimented with different neural network architectures on document image dataset. We discuss our preliminary results in this post.

Deep Image Prior defies the idea that “deep learning only works in the context of massive datasets or models pretrained on such datasets”. This paper showed that some deep neural networks could be successfully trained on a single image without large datasets, the structure of the network itself could be preventing deep networks from overfitting.

This post presents a walk through of an object detection process applied to Audio/Video receiver back panel images. ^{1}

Two transaction models dominate the blockchain world today - Account-based model where transactions are modeled as transfers happening from or to per-user accounts, and “unspent transaction output” or UTXO-based transactions. This post dives into the UTXO model, its design, execution and properties.

This is a short introduction to zero knowledge proofs (ZKP), along with some motivating examples. The aim of this post is to generate curiosity among the readers for this upcoming new area of research. After giving the readers an intution of what zero knowledge proofs are, In the next posts I would then get into some technical deep dives to show how ZKP is used in the wild.

Age verification is an age old problem. Lots of places require you to prove that you are above a certain age to guarantee certain services, be it for issuing a drivers licence, generating a voter id etc. The current way of doing so reveals a lot of information about the user. We want to be able to do the same using Zero Knowledge Proof.

Unsupervised topic modeling algorithms like LDA and NMF produces list of vocabularies for each topic after the training. These vocabs help human to assign the subject information of the topic model. So how we measure the quality of these topic words ?, this problem has to be addressed in unsupervised topic clustering algorithms like LDA / NMF to understand models are improving or not.
It’s always a challenge to qualitatively measure the goodness of the words produced by each topic.

Sharing a small page I cooked up to help people explore transformations - both the linear and the non-linear kind by drawing pictures and modifying them using transformations. This hack is in the spirit of the theme of this year’s Infinity festival at Pramati - where we have “Engaging the senses” as a theme. So, engage your senses to grasp the math of transformations.

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