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...

SaaS (Software as a Service) is a cloud computing model where users access software applications over the internet without installing or maintaining them on their own hardware. Instead of purchasing and managing licenses or handling complex installations, organizations and individuals...

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of evaluation metrics widely used in natural language processing (NLP) to assess automatic summarization, machine translation, and text generation systems. It measures the overlap between machine-generated outputs and human-written references, focusing...

RNN (Recurrent Neural Network) is a type of neural network designed to process sequential data. Unlike traditional feedforward networks, RNNs maintain a hidden state that captures information about previous inputs, making them ideal for tasks involving time-series, language, or any...

RLHF (Reinforcement Learning from Human Feedback) is a technique used to fine-tune machine learning models, particularly large language models, using human feedback as a guiding signal. Rather than relying solely on static datasets, RLHF incorporates human preferences to train models...