Tree of Thoughts is a reasoning framework designed for large language models (LLMs) that guides them to explore multiple reasoning paths before settling on a final answer. Rather than following a single, linear chain of thought, the model branches out...

Transformer Model is a neural network architecture introduced in the 2017 paper “Attention is All You Need.” It serves as the foundation for most modern AI systems. Unlike older recurrent models, transformers process input in parallel. They use self-attention to...

Transfer Learning is a machine learning technique that allows a model trained on one task or domain to be repurposed for another, often related, task or domain. Instead of starting from scratch, the model leverages the knowledge and patterns it...

Training datasets are collections of data used to teach machine learning models the patterns and relationships needed to perform specific tasks. These datasets consist of input features along with corresponding labels or target values, allowing algorithms to learn from examples....

Tokenization is the process of breaking down text, speech, or other inputs into smaller units called tokens. These tokens serve as the basic building blocks that AI models use to understand and generate language. Importantly, tokenization plays a critical role...

TensorFlow is an open-source machine learning framework developed by Google. It allows developers to build, train, and deploy machine learning and deep learning models across various platforms. TensorFlow provides flexible tools for designing complex neural networks and simplifies large-scale AI...

Synthetic Data refers to artificially generated information that imitates real-world data. It is created using algorithms or simulations instead of being collected from real-world events. Consequently, synthetic data plays a crucial role in training, testing, and validating machine learning models...

Supervised Learning is a machine learning paradigm where models are trained using labeled examples. Each training instance comes with input features and an associated correct output (label), enabling the model to learn the relationship between inputs and outputs. Over time,...

sLLM (Small Language Model) refers to a lightweight AI model designed to perform language tasks while consuming fewer resources compared to large language models (LLMs). sLLMs aim to deliver strong performance on specific tasks while being more efficient, interpretable, and...

Segment Anything Model (SAM) is an advanced AI model developed by Meta AI that enables automatic segmentation of any object in an image. Unlike traditional segmentation models trained for specific object categories, SAM can generalize across a wide range of...