How to build a GPT model?

IDP is an AI powered document processing technique that not just scans and captures structured, unstructured and semi-structured data, but also understands it deeply.

Decision Transformers stand as a beacon of progress in the reinforcement learning landscape, poised to reshape how intelligent agents are trained and interact with their environments.

Generative AI is a technology used to generate new and original content. It leverages advanced algorithms and neural networks to autonomously produce outputs that mimic human creativity and decision-making.

Synthetic data is artificially generated data replicating real data’s statistical properties and characteristics without containing sensitive information.

LangChain is a framework that provides a set of tools, components, and interfaces for developing LLM-powered applications.

Data annotation is adding labels or tags to a training dataset to provide context and meaning to the data.

Parameter-efficient Fine-tuning (PEFT) is a technique used in Natural Language Processing (NLP) to improve the performance of pre-trained language models on specific downstream tasks.

Language models are the backbone of natural language processing (NLP) and have changed how we interact with language and technology.

Auto-GPT is an autonomous tool that allows large language models (LLMs) to operate autonomously, enabling them to think, plan and execute actions without constant human intervention.

EDA allows for exploring and examining data for important insights before the actual data analysis process begins. Check out this article to explore the importance, process and techniques of EDA.

By understanding the architecture of generative AI, enterprises can make informed decisions about which models and techniques to use for different use cases.

Reinforcement learning from human feedback (RLHF) is a machine learning approach that leverages a combination of human feedback and reinforcement learning to train AI models.