Posts

Small Language Models (SLMs) are a specialized type of artificial intelligence designed for natural language processing (NLP) tasks. Unlike Large Language Models (LLMs), which are characterized by their vast size and extensive training datasets, SLMs are built to be more efficient and effective for specific applications.

Quiet Eye (QE) is a fascinating phenomenon that involves a period of extended visual attention, which significantly enhances the control and execution of motor skills, especially in high-pressure situations. This technique has been shown to improve performance across various domains, including sports and surgical training, by allowing individuals to focus on critical details just before executing a movement.

Temporal Difference (TD) Learning is a fundamental concept in the field of reinforcement learning, which is a subfield of artificial intelligence (AI). It is particularly powerful for problems where an agent must learn to make decisions over time based on its interactions with an environment. Unlike traditional supervised learning, where a model learns from a fixed dataset, TD Learning enables agents to learn directly from experience, making it well-suited for dynamic and uncertain environments.

In this blog post, we explore the topic of image generators and their detection techniques. I’ll discuss various methods for detecting image generators and their manipulations. These include analyzing the visual content of an image, examining its metadata, and using machine learning algorithms to identify patterns in the data.

Vision Transformers (ViTs) are redefining image recognition by using Transformer models to capture global context, unlike traditional Convolutional Neural Networks (CNNs) that focus on local features. ViTs excel with large datasets and show impressive scalability and performance.

As a developer, you might have come across the misconception that writing code without classes in a language that supports Object-Oriented Programming (OOP) automatically makes it functional. In reality, this code is more likely procedural.

ImageNet is more than just a dataset. The sheer scale of ImageNet, combined with its detailed labeling, made it essentially the backbone of Computer Vision.

This blog post explains how self-attention and softmax function in Transformer models, crucial for modern NLP. It breaks down how self-attention helps models understand relationships between tokens and how softmax ensures efficient computation and numerical stability.

Generative AI has come a long way, producing stunning images from simple text prompts. But how do Diffusion and Auto-Regressive models work, and why are diffusion models preferred.

The Black-Scholes-Merton equation is a model for pricing options. This equation revolutionized finance by providing a precise method for determining fair option prices, improving risk management and trading efficiency.