Gradient descent is one of the most popular techniques in Data Science today. Using it, we can find optimal parameters and weights with fairly high accuracy and achieve greater accuracy in predicting data.
For the last decade, Gradient Descent has been a widely used technique for optimizing linear regression models, and with Python it’s easier than ever to implement. Essentially, gradient descent involves iteratively adjusting the coefficients of a linear regression model based on the error between its predictions and training data. In practice, this means gradually stepping towards the optimal set of coefficients that minimizes this error.
With Python libraries like NumPy and Pandas, implementing gradient descent becomes straightforward. You can easily load in your training data, initialize your model parameters, and run an iterative algorithm that performs the necessary coefficient updates. In spite of its simplicity though, gradient descent remains a powerful tool for optimizing linear regression models, making it an essential technique for any data scientist or machine learning practitioner to have in their toolkit!
Today there are many Python libraries and functions for simple gradient descent calculation. But simplе it’s not always interesting. I was interested in understanding the functions of this algorithm internally, and the link below will give you an inside look at how gradient descent works.
