NLP

The Business Case For AI: A Review by Customer Contact Week Conference & Magazine

“Stop using AI.” This is how Dr. Kavita Ganesan, an AI expert since 2005, begins her book The Business Case for AI. In a refreshingly direct tone, Ganesan goes on to deliver the news that, yes, you probably need to rethink your use of AI and, no, it does not need to be this difficult or this expensive. While AI is a necessary tool for businesses to remain competitive, many find themselves worried about the investment, and the consequences– what […]

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NLP

What are Precision & Recall in Machine Learning?

Precision and recall are commonly used metrics to measure the performance of machine learning models or AI solutions in general. It helps understand how well models are making predictions. Let’s use an email SPAM prediction example. Say you have a model that looks at an email and decides whether it’s SPAM or NOT SPAM. To see how well it’s doing, you want to compare it with human-generated labels, which we will call the actual labels. To demonstrate this, the table […]

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Python

My “Aha Moment” About Python

I’ve been programming for about 20 years now, and my “aha moment” with python came to me last night. It was a pretty simple one — I started with Python 2 because that’s what we’re using at work (and it was free). But after some trial and error, I figured out how to get started quickly with Python 3.

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Python

The only cheat sheet you need

You want to sort a list in python or any other programming language and suppose you don’t know how to do it, using the cheat.sh, you just need to give keywords to the program, and it will automatically give you the best code to sort the list concerning minimum time complexity i.e., Big-O. If you do not want to install cheat.sh for offline usage, you can use the live link, where you can search your queries. For Windows users, if […]

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Natural Language Processing, Python

Knowledge Graph Attention Network for Recommendation

There are three main parts to this system: the embedding layer, which preserves the CKG’s structure and parameterizes each node as a vector; the attentive embedding propagation layer, which updates a node’s representation by recursively propagating embeddings from its neighbors and uses a knowledge-aware attention mechanism to learn the weight of each neighbor during a propagation; and the prediction layer, which combines the representations of a user and an item from all propagation layers, and outputs the predicted score.

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