Post-training refers to the stage that follows the initial model training process, focusing on refining, optimizing, and preparing the model for deployment in real-world scenarios. After a model has been trained—often through pre-training and fine-tuning—it may still benefit from additional...

In the context of natural language processing (NLP) and machine learning, perplexity is a metric used to evaluate the performance of language models. It measures how well a model predicts a sequence of words, with lower perplexity indicating better performance....

Parameter refers to the internal variables of a machine learning model that are learned from training data. These values define how input features are transformed into outputs, and they are crucial for determining the model’s behavior and accuracy. Parameters are...

Open Source refers to software or projects where the source code is made publicly available, allowing anyone to view, modify, and distribute it. Open-source development promotes collaboration, transparency, and community-driven innovation. Many foundational technologies in AI, cloud computing, and web...

Node refers to a fundamental unit used across various fields of computing and networking. In computer science, it typically represents a single point within a data structure, network, or system. Depending on the context, a node can serve as a...

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. It combines linguistics, machine learning, and computational techniques to process and analyze natural language data, such as text...

Neural Network refers to a class of machine learning models inspired by the structure and function of the human brain. These networks consist of layers of interconnected nodes (neurons) that process and transform data to perform tasks like classification, prediction,...

Multimodal refers to machine learning systems that process and integrate information from multiple types of data, or “modalities,” such as text, images, audio, video, and structured data. Multimodal models aim to leverage the complementary strengths of different data types to...

MNIST Dataset is a widely used benchmark in machine learning and computer vision that contains 70,000 images of handwritten digits (0-9). Each image is a 28×28 grayscale pixel grid, making it a standard for evaluating the performance of image classification...

MLOps refers to a set of practices that combines machine learning (ML) and operations (Ops) to streamline the deployment, monitoring, and maintenance of ML models in production environments. By applying DevOps principles to machine learning workflows, MLOps ensures that models...