Our Blog
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Agentic AI
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. What is Agentic AI? In short, Agentic AIs are systems designed to operate autonomously, make decisions, and perform tasks without human intervention. These systems have…
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Named Entity Recognition for Impact Evaluation
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, built upon the state-of-the-art mDeBERTa-v3 architecture, achieves unparalleled performance in identifying causal entities and extracting valuable knowledge from economic research papers.
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Machine Learning in Production: Key Challenges
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 do it and maybe even what comes after. To efficiently do it, while understanding the reasoning behind certain decisions, is another thing though. Lets take…
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RNNs: Why Not Standard Networks?
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 Sequence Models series. The next article discusses RNNs in greater detail. Basically, there are a couple of main problems that come up when using Standard…
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Sequence Models
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 kinds of sequential data, like textual data, time series data, audio signals, video streams or any ordered data. These sequences are usually of varying lengths…
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What is Machine Learning?
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 make decisions based on data. It’s a question that sparks curiosity widely – “What is Machine Learning?” – and it’s one that both beginners and…
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What is zk-SNARK?
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 has witnessed a groundbreaking solution to these obstacles. In this blog post, we will explore zk-SNARKs, their underlying principles, applications, and the transformative impact they…
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Generative AI: The Magic of AI
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 anymore. Thanks to generative AI, a subfield of artificial intelligence that can generate new data instances that resemble human-created content, these tasks are now possible…
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How to Ace Categorical Variables in Data Science
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 and transforming data to optimizing performance and achieving success. There are three main ways to handle categorical variables: Drop Categorical Variables This is the first and simplest method. It works by dropping categorical variables directly from…
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Handle Missing Data In Machine Learning
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 to handling missing values, and it is important to choose the most appropriate one for your dataset and machine learning task. In this article, we…