If you have been following my previous tutorials, you must be familiar with other optimization techniques to solve ML problems. In this tutorial, I am going to introduce you to a numerical optimization technique called stochastic gradient descent which is widely used in modern machine-learning models such as neural nets. This tutorial is very important and will serve as the foundation for deep learning-related tutorials. Please don’t skip this.
Now, why does SGD stand out? Well, unlike some other methods that might be a bit slow and meticulous, SGD is like a speedy learner. It adapts quickly, updating its knowledge with every new example it sees. This makes it stand out in the crowd, especially when dealing with massive datasets where efficiency matters.
Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to fitting linear classifiers and regressors under convex loss functions such as (linear) Support Vector Machines and Logistic Regression. Even though SGD has been around in the machine learning community for a long time, it has received a considerable amount of attention just recently in the context of large-scale learning. SGD has been successfully applied to large-scale and sparse machine-learning problems often encountered in text classification and natural language processing. Strictly speaking, SGD is merely an optimization technique and does not correspond to a specific family of machine learning models. It is only a way to train a model
Sklearn
Table of Contents
Prerequisites
- Linear Algebra
- Calculus
- Probability & Statistics
- Python
- Familiarity with Classical Machine Learning Models
What You Will Learn
- Advanced Calculus Basics For SGD
- Batch GD
- True SGD
- Mini-Batch SGD
- Momentum SGD or SGDM
- Adaptative Gradient (AdaGrad)
- RMSprop
- Adam
- SGD Application in Sklearn
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