Definition
Gradient descent is an optimization algorithm widely used to train machine learning models and neural networks by minimizing errors between predicted and actual results. As models process training data, the cost function measures accuracy with each parameter adjustment, guiding the learning process over time. In other words, the cost function represents the discrepancy between the predicted output of the model and the actual output. The goal of gradient descent is to find the parameters that minimize this discrepancy and improve the model’s performance. Until the function is close to or equal to zero, the model will continue to adjust its parameters to yield the smallest possible error [1].
For example, lets imagine you are a traveler that lost his way in the hills of Obudu Mountain Resort. Basically, it will be a matter of finding the way back to the valley below by first looking for the direction with a steep downward slope. After having followed this direction for a certain distance, this method must be repeated until the resort base is reached (the lowest point). In machine learning, the gradient descent consists of repeating this method in a loop until finding a minimum for the cost function. This is why it is called an iterative algorithm and why it requires a lot of calculation.
Illustration of the gradient descent concept [2].
Origin
The origin of gradient descent can be traced back to 1847 when Augustin-Louis Cauchy proposed the first form of gradient descent for solving systems of equations. Then with the rise of neural networks during the period from 1960s to 80s, researchers adopted gradient descent for backpropagation. The deep learning revolution in the 2010s made gradient descent central, especially in stochastic and mini-batch forms. Today, every major AI system — from AlphaGo to GPT-4 — is trained using variants of gradient descent [3].
Context and
Usage
Some of the
applications of gradient descent can be seen in the following:
- Linear Regression and Logistic Regression: It is used to improve weight parameters to reduce error in regression and classification models.
- Neural Networks: It assists in training deep learning models via adjusting weights through backpropagation.
- Natural Language Processing (NLP): Optimizes word embeddings and language models for better text representation.
- Reinforcement Learning: Used for policy optimization to improve decision-making in agents.
Why it Matters
Have you ever
wondered or asked the question, “How do machine learning models learn? That is
where the algorithm called gradient descent comes in. It is very important and
necessary for anyone working in the fields of data science, artificial
intelligence, or deep learning to have a good understanding of what is gradient
descent in machine learning.
According to Andrew
Ng, founder of DeepLearning.AI, “Gradient descent is not just a technique, but
the foundation of machine learning optimization.” Whether you’re training a
neural network or fine-tuning a regression model, this mathematical technique
ensures your model makes accurate predictions over time [4].
Related Model
Training and Evaluation Concepts
- Hyperparameter: Configuration setting defined before training that controls the learning process
- Hyperparameter Tuning: Process of finding optimal hyperparameter values to improve model performance
- Inference: Process of using a trained model to make predictions or generate outputs on new data
- Instruction Tuning: Training method that teaches models to follow specific instructions and commands
- Loss Function: Mathematical measure of how far a model's predictions are from actual values
In Practice
One good example
of a real-life case study of the use of Gradient Descent is Google’s AlphaGo
project. Through the utilization of gradient-based optimization, AlphaGo
achieved exceptional performance in complex decision-making scenarios, such as
the board game Go [5].
Reference
- IBM. (n.d). What is gradient descent?
- Geeksforgeeks. (2026). What is Gradient Descent.
- Fahey, J. (2025). Gradient Descent: The Engine Behind Modern AI.
- Amritha K. (2025). What Is Gradient Descent in Machine Learning? A Must-Know Guide for Beginners.
- Lyzr Team. (2025). Gradient Descent.