Author: Ashutosh Pathak
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Agentic AI
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Agentic AI is the talk of the town and every company wants a piece of it. Here’s my understanding of the topic and how it can be useful to you.…
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Named Entity Recognition for Impact Evaluation
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In this article, we’ll dive deep into the inner workings of EconBERTa, exploring how it tackles the challenges of named entity recognition in the economics domain. Discover how this model,…
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Machine Learning in Production: Key Challenges
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Have you wondered what happens when a machine learning model is deployed to production and is used extensively? If you have deployed to prod yourself, you probably know how to…
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RNNs: Why Not Standard Networks?
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In this article, I discuss the motivations behind the requirement of Recurrent Neural Networks outlining why we need them in the first place. This article is the second in the…
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Sequence Models
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Sequence models are a class of machine learning models designed for the tasks that involve sequential data, where the order of elements in the input is important. There are many…
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What is Machine Learning?
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Machine Learning (ML) is an exciting and ever-evolving field that sits at the intersection of computer science and statistics, offering tools and techniques that enable computers to learn from and…
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What is zk-SNARK?
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Introduction In the fast-paced world of blockchain technology, privacy and scalability remain two critical challenges. However, with the emergence of zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARK), the blockchain industry…
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Generative AI: The Magic of AI
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Imagine being able to create realistic images of people who don’t exist, catchy headlines for any topic, or original music in any style. Sounds like science fiction, right? Well, not…
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How to Ace Categorical Variables in Data Science
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Processing categorical variables in machine learning can be a complex and daunting task, but it is essential to unlock the full potential of your data. This article covers best practices for working with categorical variables, from encoding…
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Handle Missing Data In Machine Learning
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Dealing with missing data is a common challenge in machine learning, as it can have a significant impact on the accuracy and reliability of the model. There are several approaches…
