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 Distillation

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?

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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.

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