Python provides a number of useful packages. One of Python's most popular Machine Learning libraries is Scikit-Learn. With a high-level API that is easy and simple to use, it is optimized and powerful. Scikit-Learn comes with a wide range of useful tools and methods that make preprocessing, evaluating, and other time-consuming chores as easy as calling a single function - and splitting data into training and testing sets is no exception.

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What Is Gradient Descent?

Gradient Descent or Steepest Descent is one of the most widely used optimization techniques for training machine learning models by reducing the difference between predicted and actual outcomes. In addition, Neural Networks can be trained via gradient descent as well.

Machine learning and deep learning approaches are built on the foundation of the Gradient Descent method.

Gradient descent is now the most widely utilized optimization approach in machine learning and deep learning. It is used to train data models, and it can be used with any method. It is pretty simple to learn and implement.

This method is frequently taught at the beginning of practically all machine learning courses due to its significance and ease of implementation.

Types of Gradient Descent Algorithm

Gradient descent is simply a machine learning technique for determining the values of a function's parameters (coefficients) that minimize a cost function to the greatest extent feasible. There are three primary gradient descent types which are as follows:

1. Batch Gradient Descent

In Batch Gradient Descent, the error for each point in the training set is found and the model is updated after evaluating all training examples.

In other words, To perform a single step in Batch Gradient Descent, all of the training data is taken into account. We take the average of all of the training samples' gradients and utilize that average gradient to update our parameters. So that's only one epoch's worth of gradient decrease.

What’s an Epoch, you might ask. An epoch is when the entire training set is passed through the model, forward propagation and backward propagation are performed and the parameters are updated.

2. Stochastic Gradient Descent

Another variant of gradient descent called stochastic gradient descent (SGD) runs one training example for each iteration. 

Deep learning models are data-hungry. The more data there is, the more likely a model is to be accurate. If our dataset contains 5 million cases, the model will need to compute the gradients of all 5 million examples in only one step. This does not appear to be an efficient method. Stochastic Gradient Descent is a solution to this problem.

Stochastic Gradient Descent, abbreviated as SGD, is used to calculate the cost function with just one observation. We go through each observation one by one, calculating the cost and updating the parameters.

3. Mini Batch Gradient Descent

The combination of batch gradient descent with stochastic gradient descent is known as Mini Batch gradient descent. It separates the training datasets into tiny batches and conducts updates on each batch individually. 

Splitting training datasets into smaller batches strikes a compromise between batch gradient descent's computational efficiency and stochastic gradient descent's speed. As a result, we may accomplish a unique form of gradient descent that is more computationally efficient and less noisy.

There’s another Category called the Secondary Gradient Descent that is relevant to higher codimension. Adding noise to the discrete gradient descent process can change the gradient descent trajectory. Such effects have been identified in prior research. In this type, we conduct some computer experiments to investigate the behavior of noisy gradient descent in the more complicated context of higher-codimension minima.

Importance of Stochastic Gradient Descent

For bigger datasets, SGD can be employed. When the dataset is large, it converges quicker since the parameters are updated more frequently.

Deep learning is largely concerned with resolving optimization problems. According to computer scientists, stochastic gradient descent, or SGD, has evolved into the workhorse of Deep Learning, which is responsible for astounding advancements in computer vision. SGD can be faster than batch gradient descent, depending on the situation. One benefit is that the regular updates enable us to track our progress in great detail.

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More About SGD Classifier In SKlearn

The Stochastic Gradient Descent (SGD) can aid in the construction of an estimate for classification and regression issues when used with regularized linear techniques.

The SGDClassifier class in the Scikit-learn API is used to implement the SGD approach for classification issues. The SGDClassifier constructs an estimator using a regularized linear model and SGD learning. The SGD classifier performs well with huge datasets and is a quick and simple approach to use.

Attributes Used By SGDClassifier

The attributes utilized by the SGDClassifier module are listed below:

  • coef_ − array, shape (1, n_features) if n_classes==2, else (n_classes, n_features). This attribute specifies how much weight each characteristic is given.
  • intercept_ − array, shape (1,) if n_classes==2, else (n_classes,) In a decision function, it represents the independent term.
  • n_iter_ − The number of iterations required to meet the stopping condition is given by intIt.

Pros and Cons of Stochastic Gradient Descent

We highlight the pros and cons of stochastic gradient descent below. 


Learning occurs on every occurrence in stochastic gradient descent (SGD), and it has a few benefits over other gradient descent methods.

  1. Since the network processes just one training sample, it is easy to put into memory.
  2. It can converge quicker for bigger datasets since the parameters are updated more often.
  3. Only one sample is processed at a time, hence, it is computationally efficient.
  4. The steps made towards the minima of the loss function include oscillations that can assist get out of the local minimums of the loss function due to frequent updates.
  5. When compared to batch gradient descent, it is rather quick to compute.


  1. The steps made towards the minima of the loss function include oscillations that can assist get out of the local minimums of the loss function due to frequent updates.
  2. Furthermore, due to noisy steps, convergence to the loss function minima may take longer.
  3. Since it only interacts with one sample at a time, it lacks the benefit of vectorized operations.
  4. All resources are used to analyze one training sample at a time, frequent updates are computationally costly.

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