In this document, we explored the application of Machine Learning (ML) in credit risk management, focusing on the development of Early Warning Systems (EWS). These systems are designed to detect early signs of credit deterioration, enabling proactive risk mitigation. Traditional
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Today in this article I will explore the fascinating evolution of Generative AI, from its early roots in rule-based systems to the current era of sophisticated deep learning models. We will trace key milestones and technologies, highlighting how AI has
Today in this document I will be sharing the transformative role of Artificial Intelligence (AI) in credit underwriting. Traditionally a labor-intensive process prone to human error, credit underwriting is being revolutionized by AI’s ability to rapidly analyze vast datasets, including
In this article, I’ve delved into the critical factors that influence the storage size of generative AI models. I began by defining model storage size and then explored how the number and precision of parameters, model architecture, and compression techniques
Recently Andrew Ng in his post shared about the falling cost of ChatGPT tokens, The cost has plummeted by approximately 80%, from $36 per million tokens in March 2023 to a mere $4 per million tokens today. Such a significant
In this exploration of the data landscape, we embark on a journey through the staggering growth of global data generation. The sheer magnitude of information created in 2024, reaching a projected 140 zettabytes, underscores the exponential nature of this phenomenon.
Today in this document I will explain the concept of recall using a confusion matrix and outlines situations where recall should be prioritized, such as in healthcare diagnostics, fraud detection, and imbalanced datasets. I will also discuss instances where recall
Generative AI-powered code autocompletion tools offer significant benefits to developers, including increased productivity, reduced errors, and faster learning. However, it’s crucial to be mindful of the potential challenges, such as over-reliance on AI, code quality issues, and security risks. By
Today I will focus on the concept of Precision in data science, particularly in the context of classification models. In the document that I am sharing, outlines when to use precision (when false positives are costly or high confidence in
Generative Adversarial Networks (GANs) are quite important in the field of Generative AI. They have revolutionized how we create and understand synthetic data. GANs have opened up new possibilities in AI, making them a crucial component of generative modeling. Today