AI Agent Frameworks

AI Model Deployment

AI Model Routing

Closed Source Model

Continual Pre-Training

Direct Preference Optimization (DPO)

Domain Adaptation

Domain-Adapted Models

Evolutionary Model Merging

Fine-Tuned Models

Gradient Descent

Knowledge-Based AI Agents

Large Action Models (LAM)

Large Language Model (LLM) Agents

Large Language Models (LLMs)

Machine Learning (ML)

Model Development

Model Distillation

Model Retraining

Multi-Agent systems (MAS)

Open Source LLMs vs Closed Source LLMs

Open Source Model

Pre-Trained Models

RAG Pros and Cons

RAG(Retrieval-Augmented Generation)

Robotic Process Automation (RPA)

Signal-to-Noise Ratio

Small Language Model (SLM) Agents

Small Language Models (SLMs)

Small Language Models (SLMs) vs Large Language Models(LLMs)

Spectrum-Powered Pre-training

Supervised Fine-Tuning (SFT)

Swarm Intelligence

Transfer Learning

Weights and Biases

Neural Network

What is Gradient Descent?

‍

Gradient Descent is the process of minimizing the loss function to optimize model behavior. The goal of Gradient Descent is to find the set of model parameters (like weights in a neural network) that result in the lowest possible loss – or in other words, the most accurate predictions.

Make your GenAI ambitions a reality with Arcee AI’s end-to-end system for merging, training, and deploying Small Language Models (SLMs).

Try our hosted SaaS, Arcee Cloud, right now – or get in touch to learn more about Arcee Enterprise.